{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b4df600",
   "metadata": {},
   "source": [
    "# Does Geography Predict Regular Public Transit Use?\n",
    "\n",
    "### A Random Forest analysis of Qualtrics latitude & longitude\n",
    "\n",
    "**Course:** PSYCH 755 · UW–Madison  \n",
    "**Project:** Communication apprehension (PRCA) persona framework  \n",
    "**Notebook role:** Secondary research question — spatial predictors of transit behavior  \n",
    "\n",
    "---\n",
    "\n",
    "#### Research question\n",
    "\n",
    "> Among Prolific↔Qualtrics **matched** respondents with complete PRCA ground truth, does approximate survey **geolocation** (`LocationLatitude`, `LocationLongitude`) predict whether an individual takes **public transportation regularly**?\n",
    "\n",
    "**Outcome (regular transit):** `Q26` ∈ {`4-8 days a month`, `8 or more days a month`}  \n",
    "**Features:** Qualtrics `LocationLatitude`, `LocationLongitude` only (primary model)  \n",
    "**Model:** Balanced Random Forest with stratified *k*-fold cross-validation  \n",
    "\n",
    "This notebook is designed for presentation: clear methods, visual EDA, formal metrics against null/country baselines, interpretable importances, and a distributable results card.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2297b1df",
   "metadata": {},
   "source": [
    "## 1. Analytic roadmap\n",
    "\n",
    "| Section | Purpose |\n",
    "|---|---|\n",
    "| **2. Setup** | Paths, styling, package imports |\n",
    "| **3. Data & sample** | Load File A/B/C matched cohort; define regular riders |\n",
    "| **4. Spatial EDA** | Where regular vs non-regular riders appear in lat/lon space |\n",
    "| **5. Model specification** | Random Forest pipeline & baselines |\n",
    "| **6. Cross-validated performance** | ROC-AUC, PR-AUC, F1, confusion matrix vs chance/country |\n",
    "| **7. Interpretation** | Gini + permutation importance; predicted probability surface |\n",
    "| **8. Artifacts** | Tables/figures/JSON for slides and the manuscript |\n",
    "| **9. Limitations** | IP geolocation caveats and confounding |\n",
    "\n",
    "Supporting code: [`src/ca_personas/geo_transit_rf.py`](../src/ca_personas/geo_transit_rf.py)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d13ce925",
   "metadata": {},
   "source": [
    "## 2. Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b85d91f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:46.483175Z",
     "iopub.status.busy": "2026-07-25T15:42:46.483051Z",
     "iopub.status.idle": "2026-07-25T15:42:47.511837Z",
     "shell.execute_reply": "2026-07-25T15:42:47.510466Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project root: /workspace\n",
      "Output dir: /workspace/outputs/geo_transit_rf\n",
      "Regular-rider Q26 labels: ['4-8 days a month', '8 or more days a month']\n",
      "Q26 stem: In the last three months on how many days did you use public transportation (bus, train, tram, etc.)?\n"
     ]
    }
   ],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "import sys\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.colors import Normalize\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "ROOT = Path.cwd()\n",
    "if ROOT.name == \"notebooks\":\n",
    "    ROOT = ROOT.parent\n",
    "SRC = ROOT / \"src\"\n",
    "if str(SRC) not in sys.path:\n",
    "    sys.path.insert(0, str(SRC))\n",
    "\n",
    "from ca_personas.geo_transit_rf import (\n",
    "    GEO_FEATURES,\n",
    "    run_geo_transit_rf_analysis,\n",
    "    save_geo_transit_rf_artifacts,\n",
    ")\n",
    "from ca_personas.load import load_full_cohort\n",
    "from ca_personas.paths import sibling_data_available\n",
    "from ca_personas.transit_ca import PRIMARY_REGULAR_LABELS, Q26_STEM\n",
    "\n",
    "# Presentation styling\n",
    "plt.rcParams.update({\n",
    "    \"figure.dpi\": 120,\n",
    "    \"savefig.dpi\": 180,\n",
    "    \"font.size\": 11,\n",
    "    \"axes.titlesize\": 13,\n",
    "    \"axes.labelsize\": 11,\n",
    "    \"axes.spines.top\": False,\n",
    "    \"axes.spines.right\": False,\n",
    "    \"axes.grid\": True,\n",
    "    \"grid.alpha\": 0.25,\n",
    "    \"legend.frameon\": False,\n",
    "})\n",
    "COLOR_REGULAR = \"#C5050C\"      # UW crimson\n",
    "COLOR_NOT = \"#3D3D3D\"\n",
    "COLOR_ACCENT = \"#047A9C\"\n",
    "\n",
    "OUT = ROOT / \"outputs\" / \"geo_transit_rf\"\n",
    "OUT.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "pd.set_option(\"display.max_columns\", 40)\n",
    "pd.set_option(\"display.float_format\", lambda x: f\"{x:0.4f}\")\n",
    "\n",
    "assert sibling_data_available(), (\n",
    "    \"Place File A/B/C under ../sibling_data/ before running this notebook.\"\n",
    ")\n",
    "print(\"Project root:\", ROOT)\n",
    "print(\"Output dir:\", OUT)\n",
    "print(\"Regular-rider Q26 labels:\", sorted(PRIMARY_REGULAR_LABELS))\n",
    "print(\"Q26 stem:\", Q26_STEM)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d8e6366",
   "metadata": {},
   "source": [
    "## 3. Data & sample construction\n",
    "\n",
    "**Sources (private sibling folder — never committed):**\n",
    "\n",
    "- `PRCAProlificExport_FileA.csv` + `FileB.csv` — stacked recruitment waves  \n",
    "- `PRCAQualtricsExport_FileC.csv` — survey responses; merge on typed Prolific ID (`Q0`)\n",
    "\n",
    "**Inclusion:** inner join with complete PRCA group + interpersonal items, non-missing latitude/longitude, and a usable `Q26` response.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0774e12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:47.513556Z",
     "iopub.status.busy": "2026-07-25T15:42:47.513370Z",
     "iopub.status.idle": "2026-07-25T15:42:53.155987Z",
     "shell.execute_reply": "2026-07-25T15:42:53.155519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"n_prolific_raw\": 262,\n",
      "  \"n_prolific_unique\": 262,\n",
      "  \"n_qualtrics_raw\": 273,\n",
      "  \"n_qualtrics_with_pid\": 255,\n",
      "  \"n_qualtrics_complete_ca\": 260,\n",
      "  \"n_joined\": 252,\n",
      "  \"n_matched_both\": 252,\n",
      "  \"n_analytic\": 241,\n",
      "  \"n_dropped_missing_pid\": 0,\n",
      "  \"n_dropped_incomplete_ca\": 11,\n",
      "  \"n_dropped_unscorable_ca\": 0,\n",
      "  \"n_dropped_unjoined\": 31,\n",
      "  \"n_prolific_only\": 10,\n",
      "  \"n_qualtrics_only\": 21,\n",
      "  \"n_qualtrics_missing_pid\": 18,\n",
      "  \"waves\": {\n",
      "    \"B\": 153,\n",
      "    \"A\": 99\n",
      "  },\n",
      "  \"notes\": [\n",
      "    \"Normalized 19 DATA_EXPIRED Student status values to missing.\",\n",
      "    \"Full Prolific waves (File A/B) omit Ethnicity / Nationality / Language; demos tier uses Age, Sex, Country of residence, and Student status.\",\n",
      "    \"Analytic sample = Prolific\\u2229Qualtrics with complete scorable PRCA group + interpersonal items.\",\n",
      "    \"Merge coverage (pre-CA filter): 252 matched Prolific\\u2229Qualtrics; 21 Qualtrics-only (incl. 18 blank Q0 test rows; disregard); 10 Prolific-only (disregard).\"\n",
      "  ]\n",
      "}\n",
      "\n",
      "Modeling N: 241\n",
      "Regular riders: 101 (41.9%)\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>participant_id</th>\n",
       "      <th>LocationLatitude</th>\n",
       "      <th>LocationLongitude</th>\n",
       "      <th>regular_transit</th>\n",
       "      <th>transit_group</th>\n",
       "      <th>Q26</th>\n",
       "      <th>Country of residence</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Employment status</th>\n",
       "      <th>Student status</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ebab12e9878344fdc93a8c1cad27ff2f709e12b90f0496...</td>\n",
       "      <td>33.4158</td>\n",
       "      <td>-119.0493</td>\n",
       "      <td>False</td>\n",
       "      <td>not_regular</td>\n",
       "      <td>2-4 days a month</td>\n",
       "      <td>United States</td>\n",
       "      <td>36.0000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b6954799ca47bc836ac02ab86e4710c3b0e096d7ace449...</td>\n",
       "      <td>34.7196</td>\n",
       "      <td>-81.5487</td>\n",
       "      <td>False</td>\n",
       "      <td>not_regular</td>\n",
       "      <td>Never</td>\n",
       "      <td>United States</td>\n",
       "      <td>41.0000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...</td>\n",
       "      <td>36.7595</td>\n",
       "      <td>-95.2947</td>\n",
       "      <td>False</td>\n",
       "      <td>not_regular</td>\n",
       "      <td>Never</td>\n",
       "      <td>United States</td>\n",
       "      <td>39.0000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...</td>\n",
       "      <td>51.9759</td>\n",
       "      <td>-1.8685</td>\n",
       "      <td>False</td>\n",
       "      <td>not_regular</td>\n",
       "      <td>Never</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>61.0000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Other</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...</td>\n",
       "      <td>36.8237</td>\n",
       "      <td>-81.2130</td>\n",
       "      <td>False</td>\n",
       "      <td>not_regular</td>\n",
       "      <td>Never</td>\n",
       "      <td>United States</td>\n",
       "      <td>45.0000</td>\n",
       "      <td>Male</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      participant_id  LocationLatitude  \\\n",
       "0  ebab12e9878344fdc93a8c1cad27ff2f709e12b90f0496...           33.4158   \n",
       "1  b6954799ca47bc836ac02ab86e4710c3b0e096d7ace449...           34.7196   \n",
       "2  6ac07c3845b0ce5cac4ab3569ea70236b7cfa8fd51576e...           36.7595   \n",
       "3  c67b3266ecd4af12979c1f67d1efffd0d3e9bfbc567d21...           51.9759   \n",
       "4  1cefb8d34b92489e8ffd9cf34c469964f0527b1cae33e0...           36.8237   \n",
       "\n",
       "   LocationLongitude  regular_transit transit_group               Q26  \\\n",
       "0          -119.0493            False   not_regular  2-4 days a month   \n",
       "1           -81.5487            False   not_regular             Never   \n",
       "2           -95.2947            False   not_regular             Never   \n",
       "3            -1.8685            False   not_regular             Never   \n",
       "4           -81.2130            False   not_regular             Never   \n",
       "\n",
       "  Country of residence     Age   Sex Employment status Student status  y  \n",
       "0        United States 36.0000  Male         Part-Time            Yes  0  \n",
       "1        United States 41.0000  Male             Other             No  0  \n",
       "2        United States 39.0000  Male         Full-Time             No  0  \n",
       "3       United Kingdom 61.0000  Male             Other             No  0  \n",
       "4        United States 45.0000  Male         Full-Time             No  0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "participants, cleaning_report = load_full_cohort(join_how=\"inner\")\n",
    "\n",
    "analysis = run_geo_transit_rf_analysis(\n",
    "    participants,\n",
    "    n_splits=5,\n",
    "    n_perm_repeats=30,\n",
    "    random_state=42,\n",
    "    grid_size=90,\n",
    ")\n",
    "frame = analysis[\"frame\"]\n",
    "summary = analysis[\"summary\"]\n",
    "\n",
    "print(json.dumps(cleaning_report, indent=2))\n",
    "print()\n",
    "print(\"Modeling N:\", summary[\"sample\"][\"n\"])\n",
    "print(\n",
    "    f\"Regular riders: {summary['sample']['n_regular']} \"\n",
    "    f\"({summary['sample']['prevalence']:.1%})\"\n",
    ")\n",
    "frame.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a94754b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.157401Z",
     "iopub.status.busy": "2026-07-25T15:42:53.157270Z",
     "iopub.status.idle": "2026-07-25T15:42:53.162204Z",
     "shell.execute_reply": "2026-07-25T15:42:53.161258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Matched analytic sample (complete CA)</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Modeling sample (complete lat/lon + Q26)</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regular transit (weekly+)</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not regular</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     metric  value\n",
       "0     Matched analytic sample (complete CA)    241\n",
       "1  Modeling sample (complete lat/lon + Q26)    241\n",
       "2                 Regular transit (weekly+)    101\n",
       "3                               Not regular    140"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample composition snapshot\n",
    "comp = pd.DataFrame([\n",
    "    {\n",
    "        \"metric\": \"Matched analytic sample (complete CA)\",\n",
    "        \"value\": cleaning_report[\"n_analytic\"],\n",
    "    },\n",
    "    {\n",
    "        \"metric\": \"Modeling sample (complete lat/lon + Q26)\",\n",
    "        \"value\": summary[\"sample\"][\"n\"],\n",
    "    },\n",
    "    {\n",
    "        \"metric\": \"Regular transit (weekly+)\",\n",
    "        \"value\": summary[\"sample\"][\"n_regular\"],\n",
    "    },\n",
    "    {\n",
    "        \"metric\": \"Not regular\",\n",
    "        \"value\": summary[\"sample\"][\"n_not_regular\"],\n",
    "    },\n",
    "])\n",
    "comp\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0444066",
   "metadata": {},
   "source": [
    "## 4. Spatial exploratory analysis\n",
    "\n",
    "We first ask whether regular and non-regular riders **occupy different regions** of the latitude–longitude plane — a necessary (not sufficient) condition for a geo-only classifier to succeed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb14e9b5",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2026-07-25T15:42:53.168185Z",
     "shell.execute_reply": "2026-07-25T15:42:53.167755Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>transit_group</th>\n",
       "      <th>n</th>\n",
       "      <th>mean_lat</th>\n",
       "      <th>std_lat</th>\n",
       "      <th>mean_lon</th>\n",
       "      <th>std_lon</th>\n",
       "      <th>median_lat</th>\n",
       "      <th>median_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>not_regular</td>\n",
       "      <td>140</td>\n",
       "      <td>41.1018</td>\n",
       "      <td>9.9998</td>\n",
       "      <td>-69.0742</td>\n",
       "      <td>45.4711</td>\n",
       "      <td>40.0215</td>\n",
       "      <td>-81.3321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>regular</td>\n",
       "      <td>101</td>\n",
       "      <td>43.0515</td>\n",
       "      <td>7.7346</td>\n",
       "      <td>-62.0013</td>\n",
       "      <td>44.2914</td>\n",
       "      <td>41.3856</td>\n",
       "      <td>-76.2681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  transit_group    n  mean_lat  std_lat  mean_lon  std_lon  median_lat  \\\n",
       "0   not_regular  140   41.1018   9.9998  -69.0742  45.4711     40.0215   \n",
       "1       regular  101   43.0515   7.7346  -62.0013  44.2914     41.3856   \n",
       "\n",
       "   median_lon  \n",
       "0    -81.3321  \n",
       "1    -76.2681  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc_group = analysis[\"descriptives_by_group\"]\n",
    "desc_group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "790a6557",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.169351Z",
     "iopub.status.busy": "2026-07-25T15:42:53.169248Z",
     "iopub.status.idle": "2026-07-25T15:42:53.171528Z",
     "shell.execute_reply": "2026-07-25T15:42:53.171083Z"
    }
   },
   "outputs": [],
   "source": [
    "if not analysis[\"descriptives_by_country\"].empty:\n",
    "    by_cty = analysis[\"descriptives_by_country\"].head(12)\n",
    "    by_cty\n",
    "else:\n",
    "    print(\"Country column not available in this frame.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e2c4fb30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.172672Z",
     "iopub.status.busy": "2026-07-25T15:42:53.172564Z",
     "iopub.status.idle": "2026-07-25T15:42:53.387772Z",
     "shell.execute_reply": "2026-07-25T15:42:53.387132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1140x696 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9.5, 5.8))\n",
    "for group, color, label in [\n",
    "    (\"not_regular\", COLOR_NOT, \"Not regular\"),\n",
    "    (\"regular\", COLOR_REGULAR, \"Regular (weekly+)\"),\n",
    "]:\n",
    "    sub = frame.loc[frame[\"transit_group\"] == group]\n",
    "    ax.scatter(\n",
    "        sub[\"LocationLongitude\"],\n",
    "        sub[\"LocationLatitude\"],\n",
    "        s=36,\n",
    "        alpha=0.75,\n",
    "        c=color,\n",
    "        edgecolors=\"white\",\n",
    "        linewidths=0.4,\n",
    "        label=f\"{label} (n={len(sub)})\",\n",
    "        zorder=3,\n",
    "    )\n",
    "ax.set_xlabel(\"Longitude\")\n",
    "ax.set_ylabel(\"Latitude\")\n",
    "ax.set_title(\"Survey geolocation of respondents by regular public-transit use\")\n",
    "ax.legend(loc=\"best\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_scatter_latlon_by_transit.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28c471ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.388852Z",
     "iopub.status.busy": "2026-07-25T15:42:53.388734Z",
     "iopub.status.idle": "2026-07-25T15:42:53.691817Z",
     "shell.execute_reply": "2026-07-25T15:42:53.691549Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1260x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10.5, 4.2))\n",
    "for ax, col, title in zip(\n",
    "    axes,\n",
    "    [\"LocationLatitude\", \"LocationLongitude\"],\n",
    "    [\"Latitude\", \"Longitude\"],\n",
    "):\n",
    "    for group, color, label in [\n",
    "        (\"regular\", COLOR_REGULAR, \"Regular\"),\n",
    "        (\"not_regular\", COLOR_NOT, \"Not regular\"),\n",
    "    ]:\n",
    "        vals = frame.loc[frame[\"transit_group\"] == group, col]\n",
    "        ax.hist(vals, bins=18, alpha=0.55, color=color, label=label, edgecolor=\"white\")\n",
    "    ax.set_title(title)\n",
    "    ax.set_xlabel(title)\n",
    "axes[0].set_ylabel(\"Respondents\")\n",
    "axes[0].legend()\n",
    "fig.suptitle(\"Marginal distributions of latitude and longitude by transit group\", y=1.02)\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_latlon_marginals_by_transit.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38ed5776",
   "metadata": {},
   "source": [
    "## 5. Model specification\n",
    "\n",
    "### Primary model\n",
    "- **Estimator:** `RandomForestClassifier` (500 trees, `min_samples_leaf=3`, `class_weight=\"balanced\"`)\n",
    "- **Features:** `LocationLatitude`, `LocationLongitude` (z-scored inside a pipeline)\n",
    "- **Validation:** stratified 5-fold CV; out-of-fold predicted probabilities\n",
    "\n",
    "### Baselines (for calibration of “predictive power”)\n",
    "1. **Prevalence / chance** — constant score equal to class prevalence (ROC-AUC = 0.5)\n",
    "2. **Majority class** — always predict the modal class\n",
    "3. **Country-only Random Forest** — same estimator family on `Country of residence` (tests whether continuous lat/long adds signal beyond coarse geography)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c7ab54b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.693245Z",
     "iopub.status.busy": "2026-07-25T15:42:53.693131Z",
     "iopub.status.idle": "2026-07-25T15:42:53.903357Z",
     "shell.execute_reply": "2026-07-25T15:42:53.902815Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary features: ['LocationLatitude', 'LocationLongitude']\n",
      "CV folds: 5\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
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       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
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       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
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       "  content: \"\";\n",
       "  width: 100%;\n",
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       "\n",
       ".sk-global div.sk-serial {\n",
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       "  flex-direction: column;\n",
       "  align-items: center;\n",
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       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
       "                (&#x27;rf&#x27;,\n",
       "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;,\n",
       "                                        min_samples_leaf=3, n_estimators=500,\n",
       "                                        n_jobs=-1, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('steps',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-steps;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=steps,-list%20of%20tuples\">\n",
       "            steps\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-steps;\">\n",
       "            steps: list of tuples<br><br>List of (name of step, estimator) tuples that are to be chained in<br>sequential order. To be compatible with the scikit-learn API, all steps<br>must define `fit`. All non-last steps must also define `transform`. See<br>:ref:`Combining Estimators &lt;combining_estimators&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">[(&#x27;scaler&#x27;, ...), (&#x27;rf&#x27;, ...)]</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('transform_input',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-transform_input;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=transform_input,-list%20of%20str%2C%20default%3DNone\">\n",
       "            transform_input\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-transform_input;\">\n",
       "            transform_input: list of str, default=None<br><br>The names of the :term:`metadata` parameters that should be transformed by the<br>pipeline before passing it to the step consuming it.<br><br>This enables transforming some input arguments to ``fit`` (other than ``X``)<br>to be transformed by the steps of the pipeline up to the step which requires<br>them. Requirement is defined via :ref:`metadata routing &lt;metadata_routing&gt;`.<br>For instance, this can be used to pass a validation set through the pipeline.<br><br>You can only set this if metadata routing is enabled, which you<br>can enable using ``sklearn.set_config(enable_metadata_routing=True)``.<br><br>.. versionadded:: 1.6</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('memory',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-memory;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=memory,-str%20or%20object%20with%20the%20joblib.Memory%20interface%2C%20default%3DNone\">\n",
       "            memory\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-memory;\">\n",
       "            memory: str or object with the joblib.Memory interface, default=None<br><br>Used to cache the fitted transformers of the pipeline. The last step<br>will never be cached, even if it is a transformer. By default, no<br>caching is performed. If a string is given, it is the path to the<br>caching directory. Enabling caching triggers a clone of the transformers<br>before fitting. Therefore, the transformer instance given to the<br>pipeline cannot be inspected directly. Use the attribute ``named_steps``<br>or ``steps`` to inspect estimators within the pipeline. Caching the<br>transformers is advantageous when fitting is time consuming. See<br>:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`<br>for an example on how to enable caching.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=verbose,-bool%2C%20default%3DFalse\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: bool, default=False<br><br>If True, the time elapsed while fitting each step will be printed as it<br>is completed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,)<br><br>The classes labels. Only exist if the last step of the pipeline is a<br>classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Only defined if the<br>underlying estimator exposes such an attribute when fit.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](2,)</td>\n",
       "           <td>[&#x27;LocationLatitude&#x27;,&#x27;LocationLongitude&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.pipeline.Pipeline.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`. Only defined if the<br>underlying first estimator in `steps` exposes such an attribute<br>when fit.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"scaler__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('copy',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-copy;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=copy,-bool%2C%20default%3DTrue\">\n",
       "            copy\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-copy;\">\n",
       "            copy: bool, default=True<br><br>If False, try to avoid a copy and do inplace scaling instead.<br>This is not guaranteed to always work inplace; e.g. if the data is<br>not a NumPy array or scipy.sparse CSR matrix, a copy may still be<br>returned.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_mean',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_mean;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_mean,-bool%2C%20default%3DTrue\">\n",
       "            with_mean\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_mean;\">\n",
       "            with_mean: bool, default=True<br><br>If True, center the data before scaling.<br>This does not work (and will raise an exception) when attempted on<br>sparse matrices, because centering them entails building a dense<br>matrix which in common use cases is likely to be too large to fit in<br>memory.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('with_std',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-with_std;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=with_std,-bool%2C%20default%3DTrue\">\n",
       "            with_std\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-with_std;\">\n",
       "            with_std: bool, default=True<br><br>If True, scale the data to unit variance (or equivalently,<br>unit standard deviation).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
       "            feature_names_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[object](2,)</td>\n",
       "           <td>[&#x27;LocationLatitude&#x27;,&#x27;LocationLongitude&#x27;]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-mean_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=mean_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            mean_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-mean_;\">\n",
       "            mean_: ndarray of shape (n_features,) or None<br><br>The mean value for each feature in the training set.<br>Equal to ``None`` when ``with_mean=False`` and ``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](2,)</td>\n",
       "           <td>[ 41.92,-66.11]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_samples_seen_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=n_samples_seen_,-int%20or%20ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            n_samples_seen_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_samples_seen_;\">\n",
       "            n_samples_seen_: int or ndarray of shape (n_features,)<br><br>The number of samples processed by the estimator for each feature.<br>If there are no missing samples, the ``n_samples_seen`` will be an<br>integer, otherwise it will be an array of dtype int. If<br>`sample_weights` are used it will be a float (if no missing data)<br>or an array of dtype float that sums the weights seen so far.<br>Will be reset on new calls to fit, but increments across<br>``partial_fit`` calls.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">float64</td>\n",
       "           <td>241</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-scale_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=scale_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            scale_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-scale_;\">\n",
       "            scale_: ndarray of shape (n_features,) or None<br><br>Per feature relative scaling of the data to achieve zero mean and unit<br>variance. Generally this is calculated using `np.sqrt(var_)`. If a<br>variance is zero, we can&#x27;t achieve unit variance, and the data is left<br>as-is, giving a scaling factor of 1. `scale_` is equal to `None`<br>when `with_std=False`.<br><br>.. versionadded:: 0.17<br>   *scale_*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](2,)</td>\n",
       "           <td>[ 9.13,44.93]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-var_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.preprocessing.StandardScaler.html#:~:text=var_,-ndarray%20of%20shape%20%28n_features%2C%29%20or%20None\">\n",
       "            var_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-var_;\">\n",
       "            var_: ndarray of shape (n_features,) or None<br><br>The variance for each feature in the training set. Used to compute<br>`scale_`. Equal to ``None`` when ``with_mean=False`` and<br>``with_std=False``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](2,)</td>\n",
       "           <td>[  83.42,2018.71]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div>\n",
       "        <div class=\"features fitted\">\n",
       "          <details>\n",
       "            <summary>\n",
       "              <div class=\"arrow\"></div>\n",
       "              <div>2 features</div>\n",
       "              <div class=\"image-container\" title=\"Copy all output features\">\n",
       "                <i class=\"copy-paste-icon\"\n",
       "                  onclick=\"\n",
       "                  event.stopPropagation();\n",
       "                  event.preventDefault();\n",
       "                  copyFeatureNamesToClipboard(this);\n",
       "                  \"\n",
       "                >\n",
       "                </i>\n",
       "              </div>\n",
       "            </summary>\n",
       "            <div class=\"features-container\">\n",
       "                <table class=\"features-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr>\n",
       "          <td>LocationLatitude</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "        <tr>\n",
       "          <td>LocationLongitude</td>\n",
       "        </tr>\n",
       "\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </div>\n",
       "          </details>\n",
       "        </div>\n",
       "    <div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually sk-global\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"rf__\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_estimators',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_estimators;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
       "            n_estimators\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_estimators;\">\n",
       "            n_estimators: int, default=100<br><br>The number of trees in the forest.<br><br>.. versionchanged:: 0.22<br>   The default value of ``n_estimators`` changed from 10 to 100<br>   in 0.22.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">500</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_leaf,-int%20or%20float%2C%20default%3D1\">\n",
       "            min_samples_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_leaf;\">\n",
       "            min_samples_leaf: int or float, default=1<br><br>The minimum number of samples required to be at a leaf node.<br>A split point at any depth will only be considered if it leaves at<br>least ``min_samples_leaf`` training samples in each of the left and<br>right branches.  This may have the effect of smoothing the model,<br>especially in regression.<br><br>- If int, then consider `min_samples_leaf` as the minimum number.<br>- If float, then `min_samples_leaf` is a fraction and<br>  `ceil(min_samples_leaf * n_samples)` are the minimum<br>  number of samples for each node.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">3</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            n_jobs: int, default=None<br><br>The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,<br>:meth:`decision_path` and :meth:`apply` are all parallelized over the<br>trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`<br>context. ``-1`` means using all processors. See :term:`Glossary<br>&lt;n_jobs&gt;` for more details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">-1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance or None, default=None<br><br>Controls both the randomness of the bootstrapping of the samples used<br>when building trees (if ``bootstrap=True``) and the sampling of the<br>features to consider when looking for the best split at each node<br>(if ``max_features &lt; n_features``).<br>See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=class_weight,-%7B%22balanced%22%2C%20%22balanced_subsample%22%7D%2C%20dict%20or%20list%20of%20dicts%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: {&quot;balanced&quot;, &quot;balanced_subsample&quot;}, dict or list of dicts,             default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one. For<br>multi-output problems, a list of dicts can be provided in the same<br>order as the columns of y.<br><br>Note that for multioutput (including multilabel) weights should be<br>defined for each class of every column in its own dict. For example,<br>for four-class multilabel classification weights should be<br>[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of<br>[{1:1}, {2:5}, {3:1}, {4:1}].<br><br>The &quot;balanced&quot; mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``<br><br>The &quot;balanced_subsample&quot; mode is the same as &quot;balanced&quot; except that<br>weights are computed based on the bootstrap sample for every tree<br>grown.<br><br>For multi-output, the weights of each column of y will be multiplied.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('criterion',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-criterion;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=criterion,-%7B%22gini%22%2C%20%22entropy%22%2C%20%22log_loss%22%7D%2C%20default%3D%22gini%22\">\n",
       "            criterion\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-criterion;\">\n",
       "            criterion: {&quot;gini&quot;, &quot;entropy&quot;, &quot;log_loss&quot;}, default=&quot;gini&quot;<br><br>The function to measure the quality of a split. Supported criteria are<br>&quot;gini&quot; for the Gini impurity and &quot;log_loss&quot; and &quot;entropy&quot; both for the<br>Shannon information gain, see :ref:`tree_mathematical_formulation`.<br>Note: This parameter is tree-specific.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_depth',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_depth;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_depth,-int%2C%20default%3DNone\">\n",
       "            max_depth\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_depth;\">\n",
       "            max_depth: int, default=None<br><br>The maximum depth of the tree. If None, then nodes are expanded until<br>all leaves are pure or until all leaves contain less than<br>min_samples_split samples.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_samples_split',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_samples_split;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_samples_split,-int%20or%20float%2C%20default%3D2\">\n",
       "            min_samples_split\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_samples_split;\">\n",
       "            min_samples_split: int or float, default=2<br><br>The minimum number of samples required to split an internal node:<br><br>- If int, then consider `min_samples_split` as the minimum number.<br>- If float, then `min_samples_split` is a fraction and<br>  `ceil(min_samples_split * n_samples)` are the minimum<br>  number of samples for each split.<br><br>.. versionchanged:: 0.18<br>   Added float values for fractions.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">2</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_weight_fraction_leaf;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_weight_fraction_leaf,-float%2C%20default%3D0.0\">\n",
       "            min_weight_fraction_leaf\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_weight_fraction_leaf;\">\n",
       "            min_weight_fraction_leaf: float, default=0.0<br><br>The minimum weighted fraction of the sum total of weights (of all<br>the input samples) required to be at a leaf node. Samples have<br>equal weight when sample_weight is not provided.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_features',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_features;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_features,-%7B%22sqrt%22%2C%20%22log2%22%2C%20None%7D%2C%20int%20or%20float%2C%20default%3D%22sqrt%22\">\n",
       "            max_features\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_features;\">\n",
       "            max_features: {&quot;sqrt&quot;, &quot;log2&quot;, None}, int or float, default=&quot;sqrt&quot;<br><br>The number of features to consider when looking for the best split:<br><br>- If int, then consider `max_features` features at each split.<br>- If float, then `max_features` is a fraction and<br>  `max(1, int(max_features * n_features_in_))` features are considered at each<br>  split.<br>- If &quot;sqrt&quot;, then `max_features=sqrt(n_features)`.<br>- If &quot;log2&quot;, then `max_features=log2(n_features)`.<br>- If None, then `max_features=n_features`.<br><br>.. versionchanged:: 1.1<br>    The default of `max_features` changed from `&quot;auto&quot;` to `&quot;sqrt&quot;`.<br><br>Note: the search for a split does not stop until at least one<br>valid partition of the node samples is found, even if it requires to<br>effectively inspect more than ``max_features`` features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_leaf_nodes;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_leaf_nodes,-int%2C%20default%3DNone\">\n",
       "            max_leaf_nodes\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_leaf_nodes;\">\n",
       "            max_leaf_nodes: int, default=None<br><br>Grow trees with ``max_leaf_nodes`` in best-first fashion.<br>Best nodes are defined as relative reduction in impurity.<br>If None then unlimited number of leaf nodes.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-min_impurity_decrease;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=min_impurity_decrease,-float%2C%20default%3D0.0\">\n",
       "            min_impurity_decrease\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-min_impurity_decrease;\">\n",
       "            min_impurity_decrease: float, default=0.0<br><br>A node will be split if this split induces a decrease of the impurity<br>greater than or equal to this value.<br><br>The weighted impurity decrease equation is the following::<br><br>    N_t / N * (impurity - N_t_R / N_t * right_impurity<br>                        - N_t_L / N_t * left_impurity)<br><br>where ``N`` is the total number of samples, ``N_t`` is the number of<br>samples at the current node, ``N_t_L`` is the number of samples in the<br>left child, and ``N_t_R`` is the number of samples in the right child.<br><br>``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,<br>if ``sample_weight`` is passed.<br><br>.. versionadded:: 0.19</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('bootstrap',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-bootstrap;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=bootstrap,-bool%2C%20default%3DTrue\">\n",
       "            bootstrap\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-bootstrap;\">\n",
       "            bootstrap: bool, default=True<br><br>Whether bootstrap samples are used when building trees. If False, the<br>whole dataset is used to build each tree.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('oob_score',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-oob_score;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=oob_score,-bool%20or%20callable%2C%20default%3DFalse\">\n",
       "            oob_score\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-oob_score;\">\n",
       "            oob_score: bool or callable, default=False<br><br>Whether to use out-of-bag samples to estimate the generalization score.<br>By default, :func:`~sklearn.metrics.accuracy_score` is used.<br>Provide a callable with signature `metric(y_true, y_pred)` to use a<br>custom metric. Only available if `bootstrap=True`.<br><br>For an illustration of out-of-bag (OOB) error estimation, see the example<br>:ref:`sphx_glr_auto_examples_ensemble_plot_ensemble_oob.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>Controls the verbosity when fitting and predicting.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`Glossary &lt;warm_start&gt;` and<br>:ref:`tree_ensemble_warm_start` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-ccp_alpha;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=ccp_alpha,-non-negative%20float%2C%20default%3D0.0\">\n",
       "            ccp_alpha\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-ccp_alpha;\">\n",
       "            ccp_alpha: non-negative float, default=0.0<br><br>Complexity parameter used for Minimal Cost-Complexity Pruning. The<br>subtree with the largest cost complexity that is smaller than<br>``ccp_alpha`` will be chosen. By default, no pruning is performed. See<br>:ref:`minimal_cost_complexity_pruning` for details. See<br>:ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py`<br>for an example of such pruning.<br><br>.. versionadded:: 0.22</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_samples',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_samples;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=max_samples,-int%20or%20float%2C%20default%3DNone\">\n",
       "            max_samples\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_samples;\">\n",
       "            max_samples: int or float, default=None<br><br>If bootstrap is True, the number of samples to draw from X<br>to train each base estimator.<br><br>- If None (default), then draw `X.shape[0]` samples irrespective of<br>  `sample_weight`.<br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max_samples * X.shape[0]` unweighted samples<br>  or `max_samples * sample_weight.sum()` weighted samples.<br><br>.. versionadded:: 0.22<br><br>.. versionchanged:: 1.9<br>    Float `max_samples` is relative to `sample_weight.sum()` instead of<br>    `X.shape[0]` for weighted samples.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-monotonic_cst;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=monotonic_cst,-array-like%20of%20int%20of%20shape%20%28n_features%29%2C%20default%3DNone\">\n",
       "            monotonic_cst\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-monotonic_cst;\">\n",
       "            monotonic_cst: array-like of int of shape (n_features), default=None<br><br>Indicates the monotonicity constraint to enforce on each feature.<br>  - 1: monotonic increase<br>  - 0: no constraint<br>  - -1: monotonic decrease<br><br>If monotonic_cst is None, no constraints are applied.<br><br>Monotonicity constraints are not supported for:<br>  - multiclass classifications (i.e. when `n_classes &gt; 2`),<br>  - multioutput classifications (i.e. when `n_outputs_ &gt; 1`).<br><br>The constraints hold over the probability of the positive class.<br><br>Read more in the :ref:`User Guide &lt;monotonic_cst_gbdt&gt;`.<br><br>.. versionadded:: 1.4</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%29%20or%20a%20list%20of%20such%20arrays\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes,) or a list of such arrays<br><br>The classes labels (single output problem), or a list of arrays of<br>class labels (multi-output problem).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimator_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=estimator_,-%3Aclass%3A~sklearn.tree.DecisionTreeClassifier\">\n",
       "            estimator_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimator_;\">\n",
       "            estimator_: :class:`~sklearn.tree.DecisionTreeClassifier`<br><br>The child estimator template used to create the collection of fitted<br>sub-estimators.<br><br>.. versionadded:: 1.2<br>   `base_estimator_` was renamed to `estimator_`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">DecisionTreeClassifier</td>\n",
       "           <td>DecisionTreeClassifier()</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimators_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=estimators_,-list%20of%20DecisionTreeClassifier\">\n",
       "            estimators_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimators_;\">\n",
       "            estimators_: list of DecisionTreeClassifier<br><br>The collection of fitted sub-estimators.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">list</td>\n",
       "           <td>[DecisionTreeC...te=1608637542), DecisionTreeC...te=1273642419), DecisionTreeC...te=1935803228), DecisionTreeC...ate=787846414), ...]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-estimators_samples_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=estimators_samples_,-list%20of%20arrays\">\n",
       "            estimators_samples_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-estimators_samples_;\">\n",
       "            estimators_samples_: list of arrays<br><br>The subset of drawn samples (i.e., the in-bag samples) for each base<br>estimator. Each subset is defined by an array of the indices selected.<br><br>.. versionadded:: 1.4</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">list</td>\n",
       "           <td>[array([107, 1..., dtype=int32), array([ 97, 1..., dtype=int32), array([199, 2..., dtype=int32), array([104, 2..., dtype=int32), ...]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-feature_importances_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=feature_importances_,-ndarray%20of%20shape%20%28n_features%2C%29\">\n",
       "            feature_importances_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-feature_importances_;\">\n",
       "            feature_importances_: ndarray of shape (n_features,)<br><br>The impurity-based feature importances.<br>The higher, the more important the feature.<br>The importance of a feature is computed as the (normalized)<br>total reduction of the criterion brought by that feature.  It is also<br>known as the Gini importance.<br><br>Warning: impurity-based feature importances can be misleading for<br>high cardinality features (many unique values). See<br>:func:`sklearn.inspection.permutation_importance` as an alternative.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](2,)</td>\n",
       "           <td>[0.49,0.51]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_classes_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_classes_,-int%20or%20list\">\n",
       "            n_classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_classes_;\">\n",
       "            n_classes_: int or list<br><br>The number of classes (single output problem), or a list containing the<br>number of classes for each output (multi-output problem).</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_outputs_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.RandomForestClassifier.html#:~:text=n_outputs_,-int\">\n",
       "            n_outputs_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_outputs_;\">\n",
       "            n_outputs_: int<br><br>The number of outputs when ``fit`` is performed.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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       "\n",
       "    element.setAttribute('title', fullParamName);\n",
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       "\n",
       "/**\n",
       " * Copy the list of feature names formatted as a Python list.\n",
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       "function detectTheme(element) {\n",
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       "        const themeName = themeNameAttr.toLowerCase();\n",
       "\n",
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       "            return \"dark\";\n",
       "        }\n",
       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
       "            return \"light\";\n",
       "        }\n",
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       "    }\n",
       "\n",
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       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
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       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
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       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
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      ],
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('rf',\n",
       "                 RandomForestClassifier(class_weight='balanced',\n",
       "                                        min_samples_leaf=3, n_estimators=500,\n",
       "                                        n_jobs=-1, random_state=42))])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Primary features:\", GEO_FEATURES)\n",
    "print(\"CV folds:\", analysis[\"summary\"][\"cv_metrics\"][\"n_splits\"])\n",
    "analysis[\"pipeline\"]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f31e471",
   "metadata": {},
   "source": [
    "## 6. Cross-validated predictive performance\n",
    "\n",
    "If latitude and longitude carry information about regular transit use, the geo-only Random Forest should achieve **ROC-AUC > 0.5** and, ideally, improve on the country-only baseline.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "70f30562",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.904523Z",
     "iopub.status.busy": "2026-07-25T15:42:53.904422Z",
     "iopub.status.idle": "2026-07-25T15:42:53.909806Z",
     "shell.execute_reply": "2026-07-25T15:42:53.909322Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>n</th>\n",
       "      <th>prevalence</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>average_precision</th>\n",
       "      <th>balanced_accuracy</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "      <th>brier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>random_forest_lat_lon</td>\n",
       "      <td>241</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5513</td>\n",
       "      <td>0.4410</td>\n",
       "      <td>0.5289</td>\n",
       "      <td>0.5311</td>\n",
       "      <td>0.4483</td>\n",
       "      <td>0.5149</td>\n",
       "      <td>0.4793</td>\n",
       "      <td>0.2731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prevalence_prob</td>\n",
       "      <td>241</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5809</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>majority_class</td>\n",
       "      <td>241</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.5809</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>random_forest_country_only</td>\n",
       "      <td>241</td>\n",
       "      <td>0.4191</td>\n",
       "      <td>0.5492</td>\n",
       "      <td>0.4476</td>\n",
       "      <td>0.5615</td>\n",
       "      <td>0.5851</td>\n",
       "      <td>0.5060</td>\n",
       "      <td>0.4158</td>\n",
       "      <td>0.4565</td>\n",
       "      <td>0.2487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        model    n  prevalence  roc_auc  average_precision  \\\n",
       "0       random_forest_lat_lon  241      0.4191   0.5513             0.4410   \n",
       "1             prevalence_prob  241      0.4191   0.5000             0.4191   \n",
       "2              majority_class  241      0.4191   0.5000             0.4191   \n",
       "3  random_forest_country_only  241      0.4191   0.5492             0.4476   \n",
       "\n",
       "   balanced_accuracy  accuracy  precision  recall     f1  brier  \n",
       "0             0.5289    0.5311     0.4483  0.5149 0.4793 0.2731  \n",
       "1             0.5000    0.5809     0.0000  0.0000 0.0000 0.2435  \n",
       "2             0.5000    0.5809     0.0000  0.0000 0.0000 0.4191  \n",
       "3             0.5615    0.5851     0.5060  0.4158 0.4565 0.2487  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = analysis[\"metrics_table\"].copy()\n",
    "show = [\n",
    "    \"model\", \"n\", \"prevalence\", \"roc_auc\", \"average_precision\",\n",
    "    \"balanced_accuracy\", \"accuracy\", \"precision\", \"recall\", \"f1\", \"brier\",\n",
    "]\n",
    "metrics[show]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f119646d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:53.910821Z",
     "iopub.status.busy": "2026-07-25T15:42:53.910715Z",
     "iopub.status.idle": "2026-07-25T15:42:54.180116Z",
     "shell.execute_reply": "2026-07-25T15:42:54.179575Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1296x516 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_m = analysis[\"summary\"][\"cv_metrics\"]\n",
    "cm = np.array([[rf_m[\"tn\"], rf_m[\"fp\"]], [rf_m[\"fn\"], rf_m[\"tp\"]]], dtype=float)\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10.8, 4.3))\n",
    "\n",
    "# Confusion matrix\n",
    "ax = axes[0]\n",
    "im = ax.imshow(cm, cmap=\"Reds\")\n",
    "ax.set_xticks([0, 1], [\"Pred: not regular\", \"Pred: regular\"])\n",
    "ax.set_yticks([0, 1], [\"True: not regular\", \"True: regular\"])\n",
    "for (i, j), val in np.ndenumerate(cm):\n",
    "    ax.text(j, i, f\"{int(val)}\", ha=\"center\", va=\"center\", fontsize=13, color=\"black\")\n",
    "ax.set_title(\"Out-of-fold confusion matrix (threshold = 0.5)\")\n",
    "\n",
    "# ROC\n",
    "ax = axes[1]\n",
    "roc = analysis[\"roc\"]\n",
    "ax.plot(roc[\"fpr\"], roc[\"tpr\"], color=COLOR_REGULAR, lw=2.2,\n",
    "        label=f\"Lat/lon RF (AUC = {rf_m['roc_auc']:.3f})\")\n",
    "ax.plot([0, 1], [0, 1], ls=\"--\", color=\"#888888\", label=\"Chance (AUC = 0.50)\")\n",
    "if analysis[\"country_baseline\"] is not None:\n",
    "    c_auc = analysis[\"country_baseline\"][\"metrics\"][\"roc_auc\"]\n",
    "    # country ROC not stored pointwise; mark AUC in legend via horizontal note\n",
    "    ax.text(0.35, 0.15, f\"Country-only RF AUC = {c_auc:.3f}\", color=COLOR_ACCENT)\n",
    "ax.set_xlabel(\"False positive rate\")\n",
    "ax.set_ylabel(\"True positive rate\")\n",
    "ax.set_title(\"ROC curve — geography-only Random Forest\")\n",
    "ax.legend(loc=\"lower right\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_rf_confusion_and_roc.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fdb14157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.181229Z",
     "iopub.status.busy": "2026-07-25T15:42:54.181090Z",
     "iopub.status.idle": "2026-07-25T15:42:54.344028Z",
     "shell.execute_reply": "2026-07-25T15:42:54.343297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 984x528 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Metric lift chart vs baselines\n",
    "plot_df = metrics.set_index(\"model\").loc[\n",
    "    [m for m in [\"random_forest_lat_lon\", \"random_forest_country_only\", \"prevalence_prob\", \"majority_class\"]\n",
    "     if m in metrics[\"model\"].values]\n",
    "]\n",
    "fig, ax = plt.subplots(figsize=(8.2, 4.4))\n",
    "vals = plot_df[\"roc_auc\"]\n",
    "colors = [COLOR_REGULAR if i == 0 else COLOR_ACCENT if \"country\" in idx else \"#777777\"\n",
    "          for i, idx in enumerate(vals.index)]\n",
    "bars = ax.barh(range(len(vals)), vals.values, color=colors, edgecolor=\"white\")\n",
    "ax.set_yticks(range(len(vals)), vals.index)\n",
    "ax.axvline(0.5, color=\"#444444\", ls=\"--\", lw=1, label=\"Chance\")\n",
    "ax.set_xlabel(\"ROC-AUC (stratified CV)\")\n",
    "ax.set_xlim(0.4, max(0.85, float(vals.max()) + 0.05))\n",
    "ax.set_title(\"Predictive power of geography vs baselines\")\n",
    "for bar, v in zip(bars, vals.values):\n",
    "    ax.text(v + 0.01, bar.get_y() + bar.get_height()/2, f\"{v:.3f}\", va=\"center\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_rf_auc_vs_baselines.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d2b4b713",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.345552Z",
     "iopub.status.busy": "2026-07-25T15:42:54.345442Z",
     "iopub.status.idle": "2026-07-25T15:42:54.347993Z",
     "shell.execute_reply": "2026-07-25T15:42:54.347558Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Verdict ===\n",
      "{\n",
      "  \"roc_auc\": 0.5512729844413012,\n",
      "  \"beats_chance_auc_0_5\": true,\n",
      "  \"beats_prevalence_baseline\": true,\n",
      "  \"delta_auc_vs_country\": 0.002050919377652005,\n",
      "  \"interpretation\": \"Lat/long Random Forest CV ROC-AUC = 0.551 (country-only AUC = 0.549). Geography shows modest signal above chance; interpret cautiously.\"\n",
      "}\n",
      "\n",
      "Primary CV metrics:\n",
      "  roc_auc              0.5513\n",
      "  average_precision    0.4410\n",
      "  balanced_accuracy    0.5289\n",
      "  f1                   0.4793\n",
      "  brier                0.2731\n"
     ]
    }
   ],
   "source": [
    "print(\"=== Verdict ===\")\n",
    "print(json.dumps(summary[\"verdict\"], indent=2))\n",
    "print()\n",
    "print(\"Primary CV metrics:\")\n",
    "for k in [\"roc_auc\", \"average_precision\", \"balanced_accuracy\", \"f1\", \"brier\"]:\n",
    "    print(f\"  {k:20s} {rf_m[k]:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78525131",
   "metadata": {},
   "source": [
    "## 7. What does the forest use? Importances & decision surface\n",
    "\n",
    "Even with only two features, impurity and permutation importances show the **relative contribution** of latitude vs longitude. The probability surface visualizes how predicted regular-transit risk varies over the observed geographic extent.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37d3af59",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.349192Z",
     "iopub.status.busy": "2026-07-25T15:42:54.349059Z",
     "iopub.status.idle": "2026-07-25T15:42:54.353946Z",
     "shell.execute_reply": "2026-07-25T15:42:54.353399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance_gini</th>\n",
       "      <th>importance_perm_mean</th>\n",
       "      <th>importance_perm_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LocationLongitude</td>\n",
       "      <td>0.5112</td>\n",
       "      <td>0.3348</td>\n",
       "      <td>0.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LocationLatitude</td>\n",
       "      <td>0.4888</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>0.0208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature  importance_gini  importance_perm_mean  \\\n",
       "0  LocationLongitude           0.5112                0.3348   \n",
       "1   LocationLatitude           0.4888                0.2600   \n",
       "\n",
       "   importance_perm_std  \n",
       "0               0.0316  \n",
       "1               0.0208  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp = analysis[\"gini_importance\"].merge(\n",
    "    analysis[\"permutation_importance\"], on=\"feature\"\n",
    ")\n",
    "imp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb159ad3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.354989Z",
     "iopub.status.busy": "2026-07-25T15:42:54.354868Z",
     "iopub.status.idle": "2026-07-25T15:42:54.542852Z",
     "shell.execute_reply": "2026-07-25T15:42:54.541954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1140x456 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(9.5, 3.8), sharey=True)\n",
    "axes[0].barh(imp[\"feature\"], imp[\"importance_gini\"], color=COLOR_REGULAR)\n",
    "axes[0].set_title(\"Gini importance\")\n",
    "axes[0].set_xlabel(\"Mean decrease in impurity\")\n",
    "axes[1].barh(\n",
    "    imp[\"feature\"],\n",
    "    imp[\"importance_perm_mean\"],\n",
    "    xerr=imp[\"importance_perm_std\"],\n",
    "    color=COLOR_ACCENT,\n",
    "    ecolor=\"#333333\",\n",
    "    capsize=3,\n",
    ")\n",
    "axes[1].set_title(\"Permutation importance (ROC-AUC)\")\n",
    "axes[1].set_xlabel(\"Mean AUC drop when shuffled\")\n",
    "fig.suptitle(\"Feature importance — latitude vs longitude\", y=1.03)\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_rf_feature_importance.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cc97e393",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.544507Z",
     "iopub.status.busy": "2026-07-25T15:42:54.544400Z",
     "iopub.status.idle": "2026-07-25T15:42:54.873346Z",
     "shell.execute_reply": "2026-07-25T15:42:54.872786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1176x696 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = analysis[\"grid\"]\n",
    "lat = np.sort(grid[\"LocationLatitude\"].unique())\n",
    "lon = np.sort(grid[\"LocationLongitude\"].unique())\n",
    "Z = grid.pivot(index=\"LocationLatitude\", columns=\"LocationLongitude\", values=\"p_regular\").values\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9.8, 5.8))\n",
    "extent = [lon.min(), lon.max(), lat.min(), lat.max()]\n",
    "im = ax.imshow(\n",
    "    Z,\n",
    "    origin=\"lower\",\n",
    "    extent=extent,\n",
    "    aspect=\"auto\",\n",
    "    cmap=\"RdYlBu_r\",\n",
    "    vmin=0,\n",
    "    vmax=1,\n",
    "    alpha=0.9,\n",
    ")\n",
    "cbar = fig.colorbar(im, ax=ax, fraction=0.03, pad=0.02)\n",
    "cbar.set_label(\"Predicted P(regular transit)\")\n",
    "for group, color, marker in [\n",
    "    (\"not_regular\", COLOR_NOT, \"o\"),\n",
    "    (\"regular\", COLOR_REGULAR, \"^\") ,\n",
    "]:\n",
    "    sub = frame.loc[frame[\"transit_group\"] == group]\n",
    "    ax.scatter(\n",
    "        sub[\"LocationLongitude\"],\n",
    "        sub[\"LocationLatitude\"],\n",
    "        s=28,\n",
    "        c=color,\n",
    "        marker=marker,\n",
    "        edgecolors=\"white\",\n",
    "        linewidths=0.35,\n",
    "        alpha=0.85,\n",
    "        label=group.replace(\"_\", \" \"),\n",
    "        zorder=3,\n",
    "    )\n",
    "ax.set_xlabel(\"Longitude\")\n",
    "ax.set_ylabel(\"Latitude\")\n",
    "ax.set_title(\"Random Forest decision surface over survey geolocation\")\n",
    "ax.legend(loc=\"best\")\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_rf_decision_surface.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b4141cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:54.875303Z",
     "iopub.status.busy": "2026-07-25T15:42:54.875160Z",
     "iopub.status.idle": "2026-07-25T15:42:55.064219Z",
     "shell.execute_reply": "2026-07-25T15:42:55.063838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reliability-style view: OOF predicted probability by true class\n",
    "oof = analysis[\"oof\"]\n",
    "fig, ax = plt.subplots(figsize=(7.5, 4.2))\n",
    "for yval, color, label in [\n",
    "    (0, COLOR_NOT, \"True: not regular\"),\n",
    "    (1, COLOR_REGULAR, \"True: regular\"),\n",
    "]:\n",
    "    ax.hist(\n",
    "        oof.loc[oof[\"y\"] == yval, \"y_prob_rf\"],\n",
    "        bins=np.linspace(0, 1, 16),\n",
    "        alpha=0.55,\n",
    "        color=color,\n",
    "        label=label,\n",
    "        edgecolor=\"white\",\n",
    "    )\n",
    "ax.axvline(0.5, color=\"#444444\", ls=\"--\", lw=1)\n",
    "ax.set_xlabel(\"Out-of-fold P(regular)\")\n",
    "ax.set_ylabel(\"Respondents\")\n",
    "ax.set_title(\"Separation of OOF predicted probabilities by true transit class\")\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "fig.savefig(OUT / \"fig_oof_probability_separation.png\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e545be",
   "metadata": {},
   "source": [
    "## 8. Export artifacts for presentation & distribution\n",
    "\n",
    "All tables, the results card, and figures are written to `outputs/geo_transit_rf/` (gitignored).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b54493a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:55.065503Z",
     "iopub.status.busy": "2026-07-25T15:42:55.065390Z",
     "iopub.status.idle": "2026-07-25T15:42:55.104094Z",
     "shell.execute_reply": "2026-07-25T15:42:55.103653Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"secondary_rq\": \"Does Qualtrics survey geolocation (latitude & longitude) predict whether a matched respondent takes public transportation regularly?\",\n",
      "  \"sample\": {\n",
      "    \"n\": 241,\n",
      "    \"n_regular\": 101,\n",
      "    \"n_not_regular\": 140,\n",
      "    \"prevalence\": 0.4190871369294606\n",
      "  },\n",
      "  \"cv_metrics\": {\n",
      "    \"n\": 241,\n",
      "    \"prevalence\": 0.4190871369294606,\n",
      "    \"threshold\": 0.5,\n",
      "    \"accuracy\": 0.5311203319502075,\n",
      "    \"balanced_accuracy\": 0.5288543140028288,\n",
      "    \"precision\": 0.4482758620689655,\n",
      "    \"recall\": 0.5148514851485149,\n",
      "    \"f1\": 0.4792626728110599,\n",
      "    \"roc_auc\": 0.5512729844413012,\n",
      "    \"average_precision\": 0.440988648630358,\n",
      "    \"brier\": 0.27307053298910217,\n",
      "    \"tn\": 76,\n",
      "    \"fp\": 64,\n",
      "    \"fn\": 49,\n",
      "    \"tp\": 52,\n",
      "    \"model\": \"random_forest_lat_lon\",\n",
      "    \"n_splits\": 5,\n",
      "    \"features\": [\n",
      "      \"LocationLatitude\",\n",
      "      \"LocationLongitude\"\n",
      "    ]\n",
      "  },\n",
      "  \"baselines\": {\n",
      "    \"prevalence_roc_auc\": 0.5,\n",
      "    \"country_only_roc_auc\": 0.5492220650636492\n",
      "  },\n",
      "  \"verdict\": {\n",
      "    \"roc_auc\": 0.5512729844413012,\n",
      "    \"beats_chance_auc_0_5\": true,\n",
      "    \"beats_prevalence_baseline\": true,\n",
      "    \"delta_auc_vs_country\": 0.002050919377652005,\n",
      "    \"interpretation\": \"Lat/long Random Forest CV ROC-AUC = 0.551 (country-only AUC = 0.549). Geography shows modest signal above chance; interpret cautiously.\"\n",
      "  },\n",
      "  \"caveats\": [\n",
      "    \"Qualtrics LocationLatitude/Longitude are approximate IP/browser geolocation, not verified home addresses.\",\n",
      "    \"Observational association \\u2260 causal effect of place on transit use.\",\n",
      "    \"Country composition and urbanicity may confound continuous lat/long effects.\"\n",
      "  ]\n",
      "}\n",
      "\n",
      "Artifacts:\n",
      "  frame                    /workspace/outputs/geo_transit_rf/geo_transit_modeling_frame.csv\n",
      "  metrics_table            /workspace/outputs/geo_transit_rf/geo_transit_rf_metrics.csv\n",
      "  oof                      /workspace/outputs/geo_transit_rf/geo_transit_rf_oof_predictions.csv\n",
      "  roc                      /workspace/outputs/geo_transit_rf/geo_transit_rf_roc_curve.csv\n",
      "  gini_importance          /workspace/outputs/geo_transit_rf/geo_transit_rf_gini_importance.csv\n",
      "  permutation_importance   /workspace/outputs/geo_transit_rf/geo_transit_rf_permutation_importance.csv\n",
      "  null_baselines           /workspace/outputs/geo_transit_rf/geo_transit_rf_null_baselines.csv\n",
      "  descriptives_by_group    /workspace/outputs/geo_transit_rf/geo_transit_descriptives_by_group.csv\n",
      "  descriptives_by_country  /workspace/outputs/geo_transit_rf/geo_transit_descriptives_by_country.csv\n",
      "  grid                     /workspace/outputs/geo_transit_rf/geo_transit_rf_prediction_grid.csv\n",
      "  summary                  /workspace/outputs/geo_transit_rf/geo_transit_rf_summary.json\n",
      "  results_card             /workspace/outputs/geo_transit_rf/geo_transit_rf_results_card.json\n",
      "Figures:\n",
      "  fig_latlon_marginals_by_transit.png\n",
      "  fig_oof_probability_separation.png\n",
      "  fig_rf_auc_vs_baselines.png\n",
      "  fig_rf_confusion_and_roc.png\n",
      "  fig_rf_decision_surface.png\n",
      "  fig_rf_feature_importance.png\n",
      "  fig_scatter_latlon_by_transit.png\n"
     ]
    }
   ],
   "source": [
    "paths = save_geo_transit_rf_artifacts(analysis, OUT)\n",
    "# Persist cleaned cohort used upstream as well\n",
    "processed = ROOT / \"data\" / \"processed\"\n",
    "processed.mkdir(parents=True, exist_ok=True)\n",
    "participants.to_csv(processed / \"participants_scored.csv\", index=False)\n",
    "(processed / \"cleaning_report.json\").write_text(json.dumps(cleaning_report, indent=2))\n",
    "\n",
    "card = json.loads(paths[\"results_card\"].read_text())\n",
    "print(json.dumps(card, indent=2))\n",
    "print()\n",
    "print(\"Artifacts:\")\n",
    "for k, p in paths.items():\n",
    "    print(f\"  {k:24s} {p}\")\n",
    "print(\"Figures:\")\n",
    "for p in sorted(OUT.glob(\"fig_*.png\")):\n",
    "    print(\" \", p.name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e86226",
   "metadata": {},
   "source": [
    "## 9. Limitations & interpretation guidance\n",
    "\n",
    "1. **Approximate geolocation.** Qualtrics `LocationLatitude` / `LocationLongitude` reflect survey/IP geolocation, not a verified residential address or transit catchment.\n",
    "2. **Observational design.** A predictive association between coordinates and regular transit use is **not** a causal effect of place.\n",
    "3. **Confounding by country / urbanicity.** Continuous lat/long may proxy national sample composition, city density, or car dependence. The country-only baseline helps contextualize—but does not eliminate—this confounding.\n",
    "4. **Outcome coarseness.** “Regular” is a thresholded reading of `Q26` (weekly-or-more). Sensitivity to alternate cutoffs is examined in `secondary_rq_transit_ca.ipynb`.\n",
    "5. **Sample scope.** Results apply to the Prolific↔Qualtrics matched analytic cohort for this project, not to a census of transit users.\n",
    "\n",
    "---\n",
    "\n",
    "### Slide-ready takeaway (auto-filled)\n",
    "\n",
    "The next cell prints AUC / conclusion lines from `summary` so this section never carries blank placeholders.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc9f5f23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-25T15:42:55.105358Z",
     "iopub.status.busy": "2026-07-25T15:42:55.105215Z",
     "iopub.status.idle": "2026-07-25T15:42:55.108853Z",
     "shell.execute_reply": "2026-07-25T15:42:55.108341Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RQ: Can lat/long predict regular public-transit use in the matched cohort?\n",
      "N / prevalence: n=241 · regular=101 · prevalence=0.419\n",
      "Lat/lon RF ROC-AUC: 0.551 · Country-only AUC: 0.549 · Chance: 0.50\n",
      "Conclusion: Geography DOES carry above-chance predictive signal for regular transit under the primary Q26 definition.\n",
      "Interpretation: Lat/long Random Forest CV ROC-AUC = 0.551 (country-only AUC = 0.549). Geography shows modest signal above chance; interpret cautiously.\n"
     ]
    }
   ],
   "source": [
    "sample = summary[\"sample\"]\n",
    "verdict = summary[\"verdict\"]\n",
    "baselines = summary[\"baselines\"]\n",
    "auc = float(verdict[\"roc_auc\"])\n",
    "country_auc = baselines.get(\"country_only_roc_auc\")\n",
    "print(\"RQ: Can lat/long predict regular public-transit use in the matched cohort?\")\n",
    "print(\n",
    "    f\"N / prevalence: n={sample['n']} · regular={sample['n_regular']} · \"\n",
    "    f\"prevalence={sample['prevalence']:.3f}\"\n",
    ")\n",
    "country_txt = (\n",
    "    f\"{float(country_auc):.3f}\"\n",
    "    if country_auc is not None and country_auc == country_auc\n",
    "    else \"n/a\"\n",
    ")\n",
    "print(f\"Lat/lon RF ROC-AUC: {auc:.3f} · Country-only AUC: {country_txt} · Chance: 0.50\")\n",
    "print(\n",
    "    \"Conclusion: Geography \"\n",
    "    + (\"DOES\" if verdict.get(\"beats_chance_auc_0_5\") else \"does NOT\")\n",
    "    + \" carry above-chance predictive signal for regular transit \"\n",
    "    \"under the primary Q26 definition.\"\n",
    ")\n",
    "# Rank lat vs lon from the permutation-importance table when present.\n",
    "if \"importances\" in analysis and isinstance(analysis[\"importances\"], pd.DataFrame):\n",
    "    imp = analysis[\"importances\"]\n",
    "    if {\"feature\", \"permutation_importance_mean\"} <= set(imp.columns):\n",
    "        ranked = (\n",
    "            imp.set_index(\"feature\")[\"permutation_importance_mean\"]\n",
    "            .reindex([\"LocationLongitude\", \"LocationLatitude\"])\n",
    "            .dropna()\n",
    "        )\n",
    "        if len(ranked):\n",
    "            print(f\"Higher permutation importance: {ranked.idxmax()}\")\n",
    "print(\"Interpretation:\", verdict.get(\"interpretation\"))\n"
   ]
  }
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