{
"cells": [
{
"cell_type": "markdown",
"id": "43ae54a5",
"metadata": {},
"source": [
"# 第 7 章 決策樹與隨機森林（Decision Tree & Random Forest）\n",
"\n",
"「醫學生的機器學習入門」第 7 章配套 notebook。\n",
"\n",
"- 建議在 **Google Colab** 執行：上方選單「執行階段 → 全部執行」即可，不需要另外安裝套件。\n",
"- 本機執行也可以（需要 numpy、pandas、matplotlib、scikit-learn）。\n",
"- 網站章節：請搭配網站「第 7 章 決策樹與隨機森林」的文字說明閱讀。\n",
"- 資料：UCI Heart Disease（Cleveland 子集，CC BY 4.0）；UCI Heart Failure Clinical Records（CC BY 4.0）。\n",
"\n",
"> 本 notebook 的醫學資料皆為公開資料集的教學示範，**僅供學習，不構成臨床建議**。圖上標籤用英文，是因為 Colab 預設沒有中文字型。"
]
},
{
"cell_type": "markdown",
"id": "f31b4dd3",
"metadata": {},
"source": [
"## 0. 載入套件、印出版本\n",
"\n",
"先確認套件版本。本 notebook 在 scikit-learn 1.6（Colab）與 1.9 都測試過。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0e71ec90",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:55.660965Z",
"iopub.status.busy": "2026-09-29T20:04:55.660888Z",
"iopub.status.idle": "2026-09-29T20:04:57.218780Z",
"shell.execute_reply": "2026-09-29T20:04:57.218006Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy 2.1.3 | pandas 2.2.3 | scikit-learn 1.6.1\n"
]
}
],
"source": [
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn\n",
"from sklearn.model_selection import (train_test_split, cross_val_score, cross_validate,\n",
" StratifiedKFold, RepeatedStratifiedKFold)\n",
"from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.inspection import permutation_importance\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"print(\"numpy\", np.__version__, \"| pandas\", pd.__version__, \"| scikit-learn\", sklearn.__version__)"
]
},
{
"cell_type": "markdown",
"id": "0e16e8de",
"metadata": {},
"source": [
"## 1. 讀入 Cleveland 心臟病資料\n",
"\n",
"直接從 UCI 讀原始檔（沒有表頭、缺值寫成 `?`）。如果網路失敗，會改用 `ucimlrepo` 套件；兩者都失敗會印出清楚的錯誤訊息。\n",
"\n",
"目標變數 `num` 原本是 0–4（血管攝影的狹窄程度等級），我們把 `num > 0` 當成「至少一條主要冠狀動脈狹窄 > 50%」＝ 1。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9f8c2c90",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:57.220843Z",
"iopub.status.busy": "2026-09-29T20:04:57.220652Z",
"iopub.status.idle": "2026-09-29T20:04:57.821962Z",
"shell.execute_reply": "2026-09-29T20:04:57.821296Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(303, 14)\n",
"遺漏值： {'ca': 4, 'thal': 2}\n"
]
},
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" age | \n",
" sex | \n",
" cp | \n",
" trestbps | \n",
" chol | \n",
" fbs | \n",
" restecg | \n",
" thalach | \n",
" exang | \n",
" oldpeak | \n",
" slope | \n",
" ca | \n",
" thal | \n",
" num | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 63.0 | \n",
" 1.0 | \n",
" 1.0 | \n",
" 145.0 | \n",
" 233.0 | \n",
" 1.0 | \n",
" 2.0 | \n",
" 150.0 | \n",
" 0.0 | \n",
" 2.3 | \n",
" 3.0 | \n",
" 0.0 | \n",
" 6.0 | \n",
" 0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 67.0 | \n",
" 1.0 | \n",
" 4.0 | \n",
" 160.0 | \n",
" 286.0 | \n",
" 0.0 | \n",
" 2.0 | \n",
" 108.0 | \n",
" 1.0 | \n",
" 1.5 | \n",
" 2.0 | \n",
" 3.0 | \n",
" 3.0 | \n",
" 2 | \n",
"
\n",
" \n",
" | 2 | \n",
" 67.0 | \n",
" 1.0 | \n",
" 4.0 | \n",
" 120.0 | \n",
" 229.0 | \n",
" 0.0 | \n",
" 2.0 | \n",
" 129.0 | \n",
" 1.0 | \n",
" 2.6 | \n",
" 2.0 | \n",
" 2.0 | \n",
" 7.0 | \n",
" 1 | \n",
"
\n",
" \n",
" | 3 | \n",
" 37.0 | \n",
" 1.0 | \n",
" 3.0 | \n",
" 130.0 | \n",
" 250.0 | \n",
" 0.0 | \n",
" 0.0 | \n",
" 187.0 | \n",
" 0.0 | \n",
" 3.5 | \n",
" 3.0 | \n",
" 0.0 | \n",
" 3.0 | \n",
" 0 | \n",
"
\n",
" \n",
" | 4 | \n",
" 41.0 | \n",
" 0.0 | \n",
" 2.0 | \n",
" 130.0 | \n",
" 204.0 | \n",
" 0.0 | \n",
" 2.0 | \n",
" 172.0 | \n",
" 0.0 | \n",
" 1.4 | \n",
" 1.0 | \n",
" 0.0 | \n",
" 3.0 | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(14, 7))\n",
"plot_tree(tree3, feature_names=list(X.columns), class_names=[\"no disease\", \"disease\"],\n",
" filled=True, rounded=True, fontsize=9, ax=ax)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "7d7e8fae",
"metadata": {},
"source": [
"也可以印成文字版的規則，很像臨床流程圖："
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "efa6efff",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:57.987502Z",
"iopub.status.busy": "2026-09-29T20:04:57.987389Z",
"iopub.status.idle": "2026-09-29T20:04:57.990404Z",
"shell.execute_reply": "2026-09-29T20:04:57.989872Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|--- cp <= 3.50\n",
"| |--- oldpeak <= 2.45\n",
"| | |--- trestbps <= 165.00\n",
"| | | |--- class: 0\n",
"| | |--- trestbps > 165.00\n",
"| | | |--- class: 1\n",
"| |--- oldpeak > 2.45\n",
"| | |--- class: 1\n",
"|--- cp > 3.50\n",
"| |--- ca <= 0.50\n",
"| | |--- thal <= 6.50\n",
"| | | |--- class: 0\n",
"| | |--- thal > 6.50\n",
"| | | |--- class: 1\n",
"| |--- ca > 0.50\n",
"| | |--- trestbps <= 109.00\n",
"| | | |--- class: 0\n",
"| | |--- trestbps > 109.00\n",
"| | | |--- class: 1\n",
"\n"
]
}
],
"source": [
"print(export_text(tree3, feature_names=list(X.columns)))"
]
},
{
"cell_type": "markdown",
"id": "ccbaa258",
"metadata": {},
"source": [
"### 手算根節點的 Gini 不純度\n",
"\n",
"根節點有 207 人，其中 112 人無狹窄、95 人有狹窄。Gini = 1 − (p₀² + p₁²)。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c055698a",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:57.992394Z",
"iopub.status.busy": "2026-09-29T20:04:57.992238Z",
"iopub.status.idle": "2026-09-29T20:04:57.995775Z",
"shell.execute_reply": "2026-09-29T20:04:57.994936Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"手算 Gini = 0.497\n",
"樹裡記錄的 Gini = 0.497\n"
]
}
],
"source": [
"n0, n1 = (y_train == 0).sum(), (y_train == 1).sum()\n",
"p0, p1 = n0 / (n0 + n1), n1 / (n0 + n1)\n",
"print(\"手算 Gini =\", round(1 - (p0**2 + p1**2), 3))\n",
"print(\"樹裡記錄的 Gini =\", round(tree3.tree_.impurity[0], 3))"
]
},
{
"cell_type": "markdown",
"id": "2e5e5692",
"metadata": {},
"source": [
"## 4. 故意做錯：不限深度的樹\n",
"\n",
"不設 `max_depth`，樹會一直切到每片葉子都純為止。用 5 折 × 5 次的交叉驗證，比較各深度的訓練分數與驗證分數。"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ef8521b4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:57.997346Z",
"iopub.status.busy": "2026-09-29T20:04:57.997249Z",
"iopub.status.idle": "2026-09-29T20:04:58.725668Z",
"shell.execute_reply": "2026-09-29T20:04:58.724910Z"
}
},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"驗證分數最高的深度： 3\n"
]
}
],
"source": [
"depths = range(1, 13)\n",
"cv_rep = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=42)\n",
"train_acc, val_acc = [], []\n",
"for d in depths:\n",
" res = cross_validate(DecisionTreeClassifier(max_depth=d, random_state=42), X, y,\n",
" cv=cv_rep, return_train_score=True)\n",
" train_acc.append(res[\"train_score\"].mean())\n",
" val_acc.append(res[\"test_score\"].mean())\n",
"\n",
"plt.plot(depths, train_acc, \"o-\", label=\"train\")\n",
"plt.plot(depths, val_acc, \"o-\", label=\"validation (CV)\")\n",
"plt.xlabel(\"max_depth\"); plt.ylabel(\"accuracy\"); plt.legend(); plt.show()\n",
"print(\"驗證分數最高的深度：\", list(depths)[int(np.argmax(val_acc))])"
]
},
{
"cell_type": "markdown",
"id": "f0b1de1e",
"metadata": {},
"source": [
"深度愈大，訓練分數逼近 100%，驗證分數卻在深度 3 左右就到頂，之後下滑——這就是過擬合（overfitting）。"
]
},
{
"cell_type": "markdown",
"id": "2e34eb5c",
"metadata": {},
"source": [
"## 5. 剪枝：用 `ccp_alpha` 讓樹變簡單\n",
"\n",
"`ccp_alpha`（cost-complexity pruning）愈大，樹被剪得愈多。先讓 sklearn 算出所有候選 alpha，再用交叉驗證挑最好的。"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4799ca5a",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:58.727802Z",
"iopub.status.busy": "2026-09-29T20:04:58.727679Z",
"iopub.status.idle": "2026-09-29T20:04:58.932524Z",
"shell.execute_reply": "2026-09-29T20:04:58.931833Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"最佳 ccp_alpha = 0.0115\n",
"剪枝後葉子數： 11 （不剪： 32 ）\n",
"剪枝後測試集準確率： 0.744\n"
]
}
],
"source": [
"path = DecisionTreeClassifier(random_state=42).cost_complexity_pruning_path(X_train, y_train)\n",
"alphas = path.ccp_alphas[:-1] # 最後一個 alpha 會剪到只剩根節點，排除\n",
"cv5 = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
"scores = [cross_val_score(DecisionTreeClassifier(ccp_alpha=a, random_state=42),\n",
" X_train, y_train, cv=cv5).mean() for a in alphas]\n",
"best_alpha = alphas[int(np.argmax(scores))]\n",
"pruned = DecisionTreeClassifier(ccp_alpha=best_alpha, random_state=42).fit(X_train, y_train)\n",
"print(\"最佳 ccp_alpha =\", round(best_alpha, 4))\n",
"print(\"剪枝後葉子數：\", pruned.get_n_leaves(), \"（不剪：\",\n",
" DecisionTreeClassifier(random_state=42).fit(X_train, y_train).get_n_leaves(), \"）\")\n",
"print(\"剪枝後測試集準確率：\", round(pruned.score(X_test, y_test), 3))"
]
},
{
"cell_type": "markdown",
"id": "bb3e7e10",
"metadata": {},
"source": [
"## 6. 隨機森林：很多棵樹投票\n",
"\n",
"`n_estimators=300` 棵樹，每棵用不同的 bootstrap 樣本、每次切分只看部分特徵（預設 `max_features=\"sqrt\"`）。`oob_score=True` 會順便算出袋外（OOB）準確率。"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1776c657",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:58.934491Z",
"iopub.status.busy": "2026-09-29T20:04:58.934396Z",
"iopub.status.idle": "2026-09-29T20:04:59.298707Z",
"shell.execute_reply": "2026-09-29T20:04:59.298006Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"訓練集準確率： 1.0\n",
"OOB 準確率： 0.826\n",
"測試集準確率： 0.833\n",
"測試集敏感度 = 0.762，特異度 = 0.896\n"
]
}
],
"source": [
"rf = RandomForestClassifier(n_estimators=300, oob_score=True, random_state=42, n_jobs=-1)\n",
"rf.fit(X_train, y_train)\n",
"print(\"訓練集準確率：\", round(rf.score(X_train, y_train), 3))\n",
"print(\"OOB 準確率： \", round(rf.oob_score_, 3))\n",
"print(\"測試集準確率：\", round(rf.score(X_test, y_test), 3))\n",
"\n",
"tn, fp, fn, tp = confusion_matrix(y_test, rf.predict(X_test)).ravel()\n",
"print(f\"測試集敏感度 = {tp / (tp + fn):.3f}，特異度 = {tn / (tn + fp):.3f}\")"
]
},
{
"cell_type": "markdown",
"id": "6ed391dd",
"metadata": {},
"source": [
"測試集只有 90 人，單次切分的分數會晃。用同一組 5 折交叉驗證公平比較三個模型："
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "fccb534c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:04:59.301000Z",
"iopub.status.busy": "2026-09-29T20:04:59.300862Z",
"iopub.status.idle": "2026-09-29T20:05:00.435188Z",
"shell.execute_reply": "2026-09-29T20:05:00.434366Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tree (no limit) 平均準確率 0.721（各折標準差 0.027）\n",
"tree (depth 3) 平均準確率 0.788（各折標準差 0.054）\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"random forest 平均準確率 0.815（各折標準差 0.056）\n"
]
}
],
"source": [
"models = {\n",
" \"tree (no limit)\": DecisionTreeClassifier(random_state=42),\n",
" \"tree (depth 3)\": DecisionTreeClassifier(max_depth=3, random_state=42),\n",
" \"random forest\": RandomForestClassifier(n_estimators=300, random_state=42, n_jobs=-1),\n",
"}\n",
"for name, m in models.items():\n",
" s = cross_val_score(m, X, y, cv=cv5)\n",
" print(f\"{name:16s} 平均準確率 {s.mean():.3f}（各折標準差 {s.std():.3f}）\")"
]
},
{
"cell_type": "markdown",
"id": "421056b2",
"metadata": {},
"source": [
"### 樹要種幾棵？看 OOB 分數隨樹數的變化\n",
"\n",
"樹很少時，有些病人剛好每棵樹都抽到、沒有 OOB 票，sklearn 會發出提醒，這裡先把該提醒關掉。"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4fa444ac",
"metadata": {
"execution": {
"iopub.execute_input": "2026-09-29T20:05:00.437189Z",
"iopub.status.busy": "2026-09-29T20:05:00.437051Z",
"iopub.status.idle": "2026-09-29T20:05:01.119963Z",
"shell.execute_reply": "2026-09-29T20:05:01.119281Z"
}
},
"outputs": [
{
"data": {
"image/png": 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SWHw8+S8dtqeGn3e5Pq+88NMCAHY0pkItaPjm2oOSZ7bo3brVDK/ukTWMLg24JjXVXc32SkzN0t1d1xoDpJ5nTXQSA4CdrLcy5N1N8vrqOB1+/tY8RFaN70r4gc1zc3XRU92Vq4c4599Xj1t7M14CEADYMIvFIks3x8stb/0s2+MviJ+Xu8wa0krm3dNWqvp6Gl0eUCJqirua6n71bvLqfnlNgS8tusAAwEapQaFTP98jP+w/o+/fVD9QXh/SWu/nBdib/s1rSp+mIRW6EnRpEIAAwAat3JsoT36xR1IycsTTzVX+0T9S7u9cT1wN+mUBlAcVdqw51f16CEAAYEPSs3Llha9/l09jT+r7TWr6y+yhrSUyxM/o0gCHQgACABuhugYmfrJTTp6/JC4uIg93bSAT+jQqtJkkgPJBAAIAg2XnmWTW6jh5b/0RvYlkaNVKMmtIa6tPCwacCQEIAAy0PzFNxi/bKfsT0/X9IVGh8sxtTcXP24PvC1CBCEAAYACz2SIfbDgqr31/QHJMZgn09ZSXB7WQ/s1D+H4AVkAAAlBiJrPFZqaw2vP1Onk+UyZ/ukt+PZKin9ercQ2Z8X8trL4VAODMCEAASmTl3gR5/uvf9a7N+dTy9moFVyMWMbPH66UWfevfLFg+jz0l6dl54uPppru7hkWHiYsa9QzAalwsaplRFJKWliYBAQGSmpoq/v7+XB04PfXL/JEl24vs45P/K9uolVzt7XpdqU2dKvLGkNYSHuRrxcoAx5ZWit/fbIUB4C+7cVRLRnG/zPPPqcfV83D965VPbWex7MGbCD+AgegCA3BdagzLld04V1O/6NXjPWeuE18v/kvJyM677vVSVPeX2tfLVlbEBZwR/1sBuC41gLckjqdkciUr4LoCcNAANHfuXHnttdckMTFRWrVqJW+//ba0b9/+ms+fPXu2vPPOOxIfHy9BQUFy5513yowZM8Tb+/LsCXW8YsUK2b9/v1SqVEk6deokr776qkRGRlrxqwIcR0lnJk39W2O9bYOz25eQJq98t/8vn8eML8CJA9Dy5ctl4sSJMn/+fOnQoYMON/369ZMDBw5IjRo1ijx/6dKlMnXqVFmwYIEONnFxcTJy5Eg9e2LWrFn6OT/99JOMGTNGoqOjJS8vT5588knp27ev/P777+Lry2BDoLTU1G012+ta3Touf8xuevDm+kyJF5EuDYNk8cZjkpiaVew4oPzrxSrPgBPPAlOhRwWVOXPm6Ptms1nCwsJk3LhxOuhcbezYsbJv3z5Zu3ZtwblJkybJ5s2bZcOGDcV+jrNnz+owpYJR165dS1QXs8CAwpZvjZcnPt9T5LIwC+z6s8CUK/+D5XoBFcsuZoHl5ORIbGys9O7d+89iXF31/U2bNhX7GtXqo16zZcsWff/IkSPy7bffyi233HLNz6MughIYeO09dbKzs/VFu/IG4E/7Ei5v0+DhVnitGtWSwRT4otSSAOq6qOvD9QJsk2FdYMnJyWIymSQ4OLjQeXVfjd8pzt13361f16VLF1ENV6qLa/To0bqbqziqRWn8+PHSuXNnad68+TVrUeOGnn/++Rv8igDHdOrCJVm6OV4fLxgRLe5urqwEXcIQ1KdpCCtnAzbK8EHQpbFu3Tp5+eWXZd68ebr77NChQ/L444/Liy++KM8880yR56uxQHv37r1m91i+adOm6bFI+VQLkOqKAyDy1pqDeq+qjvWryc0R1bkkpaC2vWCqO2CbDAtAagaXm5ubJCUlFTqv7oeEFL8ZoAo59913n4waNUrfb9GihWRkZMhDDz0kTz31lO5Cu3K80P/+9z9Zv369hIaGXrcWLy8vfQNQ2NHkDPls+0l9PLlfBJcHgMMwbAyQp6entGvXrtCAZtVlpe537Nix2NdkZmYWCjmKClFK/lhu9VGFny+++EJ++OEHqVevXoV+HYAjm70mTq9s3COyurSre+1xdABgbwztAlPdTiNGjJCoqCi99o+aBq9adGJiYvTjw4cPl9q1a+sxOsqAAQP0dPc2bdoUdIGpViF1Pj8IqW4vNV3+v//9r/j5+en1hRQ1KlytCwSgZA4kpstXu07r40l9WUcLgGMxNAANHTpUT1OfPn26DiqtW7eWlStXFgyMVosdXtni8/TTT+s1f9THU6dOSfXq1XX4eemllwqeoxZJVLp3717ocy1cuFCvGQSgZF5fdUBUw+otLUKkee0ALhsAh8Ju8MVgHSA4u10nLsgdc38RVxeRVRO6SsMafkaXBACOsQ4QANs1c9UB/XFg69qEHwAOiQAEoJDNR87JzweTxd3VRcb3ZuYXAMdEAAJQQM2ifH1VnD4eEh0mdar5cHUAOCQCEIAC6w8my5ZjKeLp7irjejbkygBwWAQgAFe0/lwe+3PfTXWlZgDLRgBwXAQgANr3vyXJ7pOp4uPpJo90b8BVAeDQCEAA9GrPs1Zfbv2J6RwuQZXZGgaAYyMAAZD/7T4tcUkXxc/bXR66mdYfAI6PAAQ4uVyTWd5YfXnm18Nd60uAj4fRJQFAhSMAAU7u89iTcuxcpgT6ekpMZzYPBuAcCECAE8vOM8lbaw/q40e7NxBfL0O3BwQAqyEAAU7s483xcjo1S4L9veTem+oaXQ4AWA0BCHBSmTl5MufHw/p4XM9G4u3hZnRJAGA1BCDASS3eeFySL2ZLWGAlGRIVZnQ5AGBVBCDACaVl5cr8ny63/ozvFaG3vgAAZ8L/eoATev/no5J6KVcaVPeVgW1qG10OAFgdAQhwMikZObJgw1F9PLFPpLi5uhhdEgBYHQEIcDLv/nRYLmbnSdOa/vK35iFGlwMAhiAAAU7kTFqWLN50TB9P7hchrrT+AHBSBCDAicz58ZBk5ZqlbZ0q0iOyhtHlAIBhCECAkziRkikfb4nXx5P7RoqLC2N/ADgvAhDgJN7+4aDkmizSqUE16dQwyOhyAMBQBCDACRw5e1E+335KH0/uF2l0OQBgOAIQ4ATeWHNQTGaL9GpcQ9rWqWp0OQBgOAIQ4OD2JaTJ17tO6+OJfSOMLgcAbAIBCHBwr6+K0x9vbVFTmtUKMLocALAJBCDAge08cUHW7EsStdzPhD60/gBAPgIQ4MBeX3VAfxzUJlQa1qhsdDkAYDMIQICD+vXIOfn5YLJ4uLnI+N6NjC4HAGwKAQhwQBaLRWZ+f7n1Z2h0mIQF+hhdEgDYFAIQ4IDWxZ2VbcfPi5e7q4ztQesPAFyNAAQ4YOtP/tif+26qKyEB3kaXBAA2hwAEOJjvf0uUvafSxNfTTR7p3sDocgDAJhGAAAeiVnvOX/fn/i71pFplL6NLAgCbRAACHMhXu07JwTMXxd/bXUbdXN/ocgDAZhGAAAeRazLLG6sP6uOHuzWQgEoeRpcEADaLAAQ4iM9iT0p8SqYEVfaUkZ3CjS4HAGwaAQhwAFm5Jnlr7eXWn0e6NxRfL3ejSwIAm0YAAhzA0s3xkpCaJTUDvOWeDnWMLgcAbB4BCLBzmTl5Mm/dIX08rmcj8fZwM7okALB5BCDAzi385ZgkX8yROoE+Mjgq1OhyAMAuEIAAO5Z6KVfe/emwPlYbnnq48SMNACXB/5aAHfvg5yOSlpUnjWpUljta1za6HACwGwQgwE6du5gtH2w4qo8n9okQN1cXo0sCALtBAALs1PyfDktGjkma1fKX/s1DjC4HAOwKAQiwQ0lpWfKfTcf18eS+keLiQusPAJQGAQiwQ3N+OCTZeWZpV7eqdI+sbnQ5AGB3CECAnTmRkinLtsbrY1p/AKBsCECAnXlz7UHJNVmkS8Mg6digmtHlAIBdIgABduTQmYuyYvtJfTypb4TR5QCA3SIAAXbkjTVxYraI9G5SQ9rUqWp0OQBgtwhAgJ34/XSafLM7QR9P7BNpdDkAYNcIQICdmLX6gP54W8ua0rSWv9HlAIBdIwABdmB7/HlZs++MqMWeJ/Rh7A8A3CgCEGAHXl91ufXn/9qGSoPqlY0uBwCcLwBlZGRUTCUAirXxcLL8cuiceLi5yGO9GnGVAMCIABQcHCz333+/bNiwoTw+P4DrsFgs8vqqOH08LLqOhAX6cL0AwIgAtGTJEklJSZGePXtKRESEvPLKK3L69OnyqAXAVdYdOCuxx8+Ll7urjO3ZkOsDAEYFoIEDB8qXX34pp06dktGjR8vSpUulbt26ctttt8mKFSskLy+vvGoDnJrZbJGZf4z9GdEpXIL9vY0uCQAcRpkHQVevXl0mTpwou3fvllmzZsmaNWvkzjvvlFq1asn06dMlMzOzfCsFnMzK3xLlt9Np4uvpJqO7NTC6HABwKO5lfWFSUpIsXrxYFi1aJMePH9fh54EHHpCTJ0/Kq6++Kr/++qusWrWqfKsFnITJbJFZqy+P/XmgSz0J9PU0uiQAcO4ApLq5Fi5cKN9//700bdpUHn30Ubn33nulSpUqBc/p1KmTNGnSpLxrBZzGf3ee0vt+BVTykFFd6xtdDgA4nFJ3gcXExOhurl9++UV27twpY8eOLRR+FPX4U089VaL3mzt3roSHh4u3t7d06NBBtmzZct3nz549WyIjI6VSpUoSFhYmEyZMkKysrBt6T8CW5JrMMnvNQX38cLf64u/tYXRJAOBwSt0ClJCQID4+15+Kq8LJs88++5fvtXz5cj2OaP78+TqoqHDTr18/OXDggNSoUaPI89WA66lTp8qCBQt0K1NcXJyMHDlSXFxc9DiksrwnYGs+2XZC4lMyJaiyl4zsFG50OQDgkErdArRu3Trd/XU1de67774r1Xup0PLggw/qViXVnaZCiwpXKuAUZ+PGjdK5c2e5++67dQtP37595a677irUwlPa9wRsSVauSd5ee0gfj+nRQHw8yzxMDwBQngFItcCYTKZiF2xTj5VUTk6OxMbGSu/evf8sxtVV39+0aVOxr1GtPuo1+YHnyJEj8u2338ott9xS5vdUsrOzJS0trdANMMKSX49LYlqW1Azwlrva1+GbAAAVpNR/Xh48eFC3rFytcePGcujQ5b9cSyI5OVkHKbWy9JXU/f379xf7GtXyo17XpUsXHbjUmkNqLaInn3yyzO+pzJgxQ55//vkS1w5UhIzsPHln3WF9rLa88PZw40IDgK20AAUEBOiWl6up8OPr6ysVSXW/vfzyyzJv3jzZvn27npH2zTffyIsvvnhD7ztt2jRJTU0tuJ04caLcagZKatHGY3IuI0fqVvORO9uFcuEAwJZagO644w4ZP368fPHFF9KgQYOC8DNp0iS5/fbbS/w+QUFB4ubmptcTupK6HxISUuxrnnnmGbnvvvtk1KhR+n6LFi305qwPPfSQnnVWlvdUvLy89A0wSuqlXHn3p8utPxN6R4iHW5nXKAUAlECp/5f917/+pVt6VJdXvXr19E2t+VOtWjWZOXNmid/H09NT2rVrJ2vXri04Zzab9f2OHTsW+xq1urQa03MlFXgU1SVWlvcEbMG/1x+RtKw8iQiuLANa1TK6HABweO5l6QJTs7FWr14tu3bt0lPeW7ZsKV27di31J1fT1UeMGCFRUVHSvn17PWVdteioGVzK8OHDpXbt2nqMjjJgwAA9y6tNmzZ6irtqeVKtQup8fhD6q/cEbE3yxWxZ8MtRfTyxT4S4uboYXRIAOLwyzbFV6+6oKejqdiOGDh0qZ8+e1XuHJSYmSuvWrWXlypUFg5jj4+MLtfg8/fTT+nOrj2ozVrUfmQo/L730UonfE7A1auBzZo5JWtQOkH7Nrt1VCwAoPy4W1XdUSqpF5aefftIBRU09v9Jjjz0m9k5Ng1ctXWpAtL+/v9HlwIElpmZJ19d+lJw8syyKiZbukSzWCQDW+P1d6hagHTt26HV31HgcFYQCAwP19HO12KBaadkRAhBgLW//cFCHn+jwqtItojoXHgBsdRC02ntLdTudP39ej/9Ru76r3eDV4OPSDIIGnF38uUxZvvXykguT+0bq7l0AgI0GILUBqpryrsbmqIHHahVltSmpmh2WvyAhgL82e22c5JktcnOjIOlQvxqXDABsOQB5eHgUDExWXV5qHJCi+txYQBAomUNn0uXLHaf08aS+kVw2ALCyUo8BUlPQt27dKo0aNZJu3brp2VZqDNCHH34ozZs3r5gqAQfzxuqDYraI9GkaLK3DqhhdDgA4nVK3AKmtKGrWrKmP1fTzqlWryiOPPKKnnr/33nsVUSPgUPaeSpVv9iSIGvIzqW+E0eUAgFMqVQuQmjGvur3yW3rUsVpjB0DJzVodpz8OaFlLGoewzAIA2HwLkApADRs2ZKwPUEaxx8/LD/vP6NWex/duxHUEAHsIQGrwsxr7c+7cuYqrCHBgM78/oD/+X9vaUr96ZaPLAQCnVeoxQK+88opMmTJF9u7dWzEVAQ5q46Fk2XTknHi4uchjvWj9AQC7mgWmNihVq0C3atVK776uFkO8UkpKSnnWBzgE1X382qrLrT93t68joVV9jC4JAJxaqQOQ2l0dQOmocT874i+It4erjOnRkMsHAPYWgEaMGFExlQAOymy2yMxVl2d+jegYLjX8vY0uCQCcXqkDUP7Kz9dSp04dp7+owJW+3Zsg+xLSpLKXu4zu1oCLAwD2GIDCw8Ovu2mjyWS60ZoAh5FnMhes+/NAl3pS1dfT6JIAAGUJQDt27Ch0Pzc3V5+bNWuWXhkawJ++3HlajpzNkCo+HjLq5npcGgCw1wCkZn9dLSoqSmrVqiWvvfaa/P3vfy+v2gC7lpNnltlrLrf+qK4vP28Po0sCAJR1HaBriYyM1JukArhs+bYTcvL8JQmq7CXDO9blsgCAPbcApaWlFVnfJCEhQZ577jm9SjQAkaxck8z54aC+FGN7NBAfz1L/qAEAKlCp/1euUqVKkUHQKgSFhYXJsmXLyrM2wG4t+fW4JKVlS+0qleSuDsyMBAC7D0A//PBDoQCk9gerXr263iTV3Z2/coGL2Xkyb91hfSEe69VQvNzduCgAYGNKnVi6d+9eMZUADmLhhqOSkpEj4dV85P/ahhpdDgCgPAZBz5gxQxYsWFDkvDr36quvlvbtAIeSmpkr7/18RB9P6BMh7m7lNs8AAFCOSv2/87vvviuNGzcucr5Zs2Yyf/788qoLsEvvrj8s6Vl5EhnsJwNa1jK6HABAeQWgxMREqVmzZpHzahyQmg0GOKvki9my8Jdj+nhi3whxdb32iukAADsLQGq21y+//FLkvDqnFkMEnNW8Hw/LpVyTtAoNkL5Ng40uBwBQnoOgH3zwQRk/frzeAqNnz5763Nq1a+Uf//iHTJo0qbRvBziEhNRLsmTzcX08qW/kdffLAwDYYQCaMmWKnDt3Th599FHJycnR57y9veWJJ56QqVOnVkSNgM17a+0hvfVF+/BAublRkNHlAAD+gotFrWJYBhcvXpR9+/ZJpUqV9ArQXl5e4ijUatcBAQGSmpoq/v7+RpcDG3f8XIb0ev0nyTNb5JOHO0r7eoFGlwQATimtFL+/S90CpN7UZDJJYGCgREdHF5xPSUnRCyESGOBs3lxzUIefrhHVCT8A4KiDoIcNG1bslheffPKJfgxwJgeT0uWLnaf08eS+EUaXAwCoqAC0efNm6dGjR7ErRKvHAGcya3WcqE7kfs2CpWVoFaPLAQBUVADKzs6WvLy8IufVrLBLly6V9u0Au7X3VKp8tzdR1ISviX0ijS4HAFCRAah9+/by3nvvFTmvVoFu165dad8OsFszVx3QH29vVUsiQ/yMLgcAUAqlHgT9z3/+U3r37i27du2SXr16FawDtHXrVlm1alVp3w6wS9uOpci6A2fFzdVFJvRm7A8AOHwLUOfOnWXTpk16RWg18Pnrr7+Whg0byu7du+Xmm2+umCoBG6JWjshv/RncLlTCg3yNLgkAUNEtQErr1q3lo48+KstLAbv3y6Fz8uuRFPF0c5VxvRoZXQ4AwFoBKF9WVlbBatD5WAcIjt7689ofrT93d6gjtatUMrokAIA1usAyMzNl7NixUqNGDfH19ZWqVasWugGObM2+M7LrxAXx9nCVR3s0MLocAIC1ApDaC+yHH36Qd955R29/8f7778vzzz+vd4L/z3/+U9Y6AJtnNlvk9T9af0Z2qic1/LyNLgkAYK0uMDXoWQUdtfBhTEyMHvisBkHXrVtXjwu65557yloLYNO+2ZMg+xPTxc/LXUZ3q290OQAAa7YAqT2/6tevXzDeR91XunTpIuvXr7+RWgCblWcyyxur4/TxqJvrSxUfT6NLAgBYMwCp8HP06FF93LhxYz0VPr9lqEoVtgKAY1qx45QcSc6Qqj4ecn+XcKPLAQBYOwCpbi+1CKIydepUmTt3rnh7e8uECRP0+CDA0WTnmfSO78robg3Ez9vD6JIAANYeA6SCTj61IvT+/fslNjZWjwNq2bLljdYD2JxPtp6QUxcuSXU/LxnekdYfABBnXwdIUYOf1Q1wRJdyTPL2D4f08bieDaWSp5vRJQEAjOgCA5zJh78ekzPp2XrBw6HRYUaXAwAoJwQg4BrSs3LlnXWH9fHjvRqJlzutPwDgKAhAwDUs2HBMzmfmSv0gX/l729pcJwBwIAQgoBgXMnPk/Z+P6OPxfSLE3Y0fFQBwJO43ujHkjz/+KJcuXZJOnTqxFxgcxrvrj0h6dp40DvGT21rUNLocAEA5K/GftRcuXJARI0ZIixYt5MEHH5S0tDS9DYaaCj9gwABp0qSJ7N69u7zrA6zuTHqWLPrlmD6e1DdSXF1d+C4AgLMGoMmTJ8umTZtk2LBhsmfPHunfv7+YTCZ9bvPmzToAPfXUUxVbLWAF8348LJdyTdIqrIr0blKDaw4AztwF9t1338nSpUulW7duMnLkSAkLC9O7wnfo0EE//uqrr8rtt99ekbUCFU4teLh0c7w+ntw3QlxcaP0BAKduAUpKSpKIiAh9XLt2bb39hQpB+erUqSNnz56tmCoBK5nzw0HJMZmlQ71A6dIwiOsOAM4egMxms7i5/bkOijq+8q9j/lKGvTuWnCGfbDupj6f0i+TfNAA4sFLNAnv//felcuXK+jgvL08WLVokQUGX/0pOT0+vmAoBK5m9Jk5MZot0j6wuUeGBXHcAcGAuFjWXvQTCw8NL9Bfx0aNHxd6pGW4BAQGSmpoq/v7+RpcDKziQmC7931wv6qfh67FdpEVoANcdABz493eJW4COHbs8LRhwRLNWH9Dhp3+zEMIPADgBlreF09tzMlW+/y1JVAPnxL6XB/oDABxbqQKQGvfz2muvSdu2bfVYIHVTxzNnzpTc3NyKqxKoQDNXHdAfB7auLRHBflxrAHACJe4CU9td9OnTRy98qFZ/7tq1qz6/b98+eeKJJ+Srr76SVatW6enxgL3YeixFfoo7K26uLjK+dyOjywEA2FoAeuWVV+TEiROyY8cOadmyZaHHdu3apRdBVM957rnnKqJOoNyp8f+vfX+59WdIVKjUrebLVQYAJ1HiLrBly5bJrFmzioQfpVWrVrobTK0UXVpz587VM8xUy5FaVXrLli3XfG737t31TLSrb7feemvBcy5evChjx46V0NBQqVSpkjRt2lTmz59f6rrg+H4+mCxbjqaIp5urjOtJ6w8AOJMSB6Djx49L+/btr/n4TTfdJPHxl7cQKKnly5fLxIkT5dlnn5Xt27frINWvXz85c+ZMsc9fsWKFJCQkFNz27t2rF2QcPHhwwXPU+61cuVKWLFmiu+fGjx+vA5HqogOubP15/Y+xP/fcVEdqVanExQEAJ1LiAKTm018rmCiJiYni51e6AaSqRUntLB8TE1PQUuPj4yMLFiwo9vmBgYESEhJScFu9erV+/pUBaOPGjXrXetVapFqWHnroIR2srteylJ2drdcOuPIGx7b69yTZdTJVKnm4yaPdGxpdDgDAVgNQjx495OWXX77m42r8j3pOSeXk5EhsbKweUF1QjKurvq8GWpfEBx98oHen9/X9c+xGp06ddGvPqVOn9F/5P/74o8TFxUnfvn2v+T4zZszQCyfl367c4wyOx2y2yKzVcfo4pnO4VPfzMrokAICtDoJW3VRqjI7q6lLdTI0bN9YBQ3UzvfHGG/L777/Lr7/+WuJPnJycLCaTSYKDgwudV/f379//l69XLTqqC0yFoCu9/fbbutVHjQFyd3fXoerf//53way14kybNk1/TflUCxAhyHF9vfu07E9MFz8vd3moa32jywEA2HIAUl1UqsvpgQce0K0u+dtiqBCkwpCaAt+sWTOxFhV8WrRoUWRckgpAKoipVqC6devK+vXrZcyYMVKrVq1CrU1X8vLy0jc4vjyTWWavOaiPH+xaX6r4eBpdEgDA1jdDVa0/v/32m54Kf/Dg5V8iERER0rp161J/YrWJqhrAnJSUVOi8uq/G91xPRkaGnpX2wgsvFFmr6Mknn5QvvviiYGaYmrW2c+dOPUvtWgEIzmPF9lNyNDlDAn095f4u9YwuBwBgT1thtGnTRnr27KlvZQk/iqenp7Rr107Wrl1bcM5sNuv7HTt2vO5rP/30Uz1w+d577y10Xq1GrW6q2+tKKmip94Zzy84zyZtrLwf3R7o1kMpepcr/AABnDUAXLlzQ3Umq9UaN1VE3daymmavHSkuNu1HjcxYvXqzHEj3yyCO6dUfNClOGDx+ux+cU1/01cOBAqVatWpGZat26dZMpU6bIunXr9M70ixYtkv/85z8yaNCgUtcHx7Jsywk5deGS1PDzkvs61jW6HACAgUr8J3BKSopumVGzq+655x5p0qSJPq8GP6uQoVpu1BT0qlWrlviTDx06VM6ePSvTp0/X0+hVa5Jawyd/YLRaV+jq1pwDBw7Ihg0b9Jij4qiuMRWaVI2qZjUO6KWXXpLRo0eXuC44nks5Jpnz4yF9PK5nQ/H2cDO6JACAgVwsahRzCagFBVXIWbNmTZGZWyq8qGnmvXr10jPC7J2aBaamw6empupWJdi/+T8dlle+2y+hVSvJD5O6i6d7mXp/AQAO8vu7xL8FvvzySz2Q+Orwo6hBy//617/04GPA1qRn5eoApDzeqxHhBwBQ8gCktp643jT35s2b65YgwNZ8sOGoXMjMlfrVfWVQm9pGlwMAsKcApAY7Hzt27JqPqwHHaqsKwJacz8iR938+qo8n9okQdze6vgAApQhAapPSp556Sm9hcTU1Jf2ZZ56R/v37c01hU+avPywXs/OkSU1/uaV5TaPLAQDY2ywwtehgVFSUNGrUSE+Fv3IrjHnz5ukQ9OGHH1ZstUApnEnLksUbL7daTuoTIa6ul1cvBwCgxAFI7a2lNil99NFH9TTz/MljakuMPn36yJw5c9g/CzZl3rrDkpVrltZhVaRXkxpGlwMAsCGlWgq3Xr168t1338n58+cLtsJo2LAhY39gc9SCh0s3x+vjKf0iC/auAwBAKdNeAGqxw6s3IQVsyVtrDkqOySwd61eTzg2DjC4HAGBjmBIDh6M2O/1s+0l9PLlfhNHlAABsEAEIDueN1XFiMlukR2R1aVeXpRkAAEURgOBQ9iemyde7T+vjSX0jjS4HAGCjCEBwKLNWxYmaoHhLixBpXjvA6HIAADaKAASHsevEBVn1e5Ko5X7Uqs8AAFwLAQgOY+aqA/rjwNa1pWENP6PLAQDYMAIQHMLmI+fk54PJ4u7qIuN70/oDALg+AhDsnlqV/PVVcfp4SHSY1KnmY3RJAAAbRwCC3Vt/MFm2HEsRT3dXGdezodHlAADsAAEIDtD6c3nsz3031ZWaAZWMLgkAYAcIQLBr3/+WJLtPpoqPp5s80r2B0eUAAOwEAQh2S632PGv15dafmM7hElTZy+iSAAB2ggAEu/W/3aclLumi+Hm7y0M30/oDACg5AhDsUq7JrPf8Uh7uWl8CfDyMLgkAYEcIQLBLn8eelGPnMiXQ11NiOtczuhwAgJ0hAMHuZOeZ5K21B/Xxo90biK+Xu9ElAQDsDAEIdmfp5ng5nZolwf5ecu9NdY0uBwBghwhAsCuZOXky98fD+nhcz0bi7eFmdEkAADtEAIJdWbzxuCRfzJawwEoyJCrM6HIAAHaKAAS7kZaVK/N/utz6M75XhN76AgCAsuA3COzG+z8fldRLudKguq8MbFPb6HIAAHaMAAS7kJKRIx/8fEQfT+wTKW6uLkaXBACwYwQg2IV3fzosGTkmaVrTX/7WPMTocgAAdo4FVGDTe31tOZoih86ky4Jfjupzk/tFiCutPwCAG0QAgk1auTdBnv/6d0lIzSo45+HmItm5ZkPrAgA4BrrAYJPh55El2wuFHyXXZJFHP9quHwcA4EYQgGBz3V6q5cdyneeox9XzAAAoKwIQbIoa83N1y8+VVOxRj6vnAQBQVgQg2JQz6Vnl+jwAAIpDAIJNqeHnXa7PAwCgOAQg2JTmtf3F4zrT3NUjNQO8pX29QKvWBQBwLAQg2Aw1sHnC8p2Se40Bzvmx6NkBTVkJGgBwQwhAsBkv/u93WbPvjN7kdEq/CN3Sc6WQAG9559620r95TcNqBAA4BhZChE1Y+MtRWbTxmD6eNaSV3Naylozu1lDP9lIDntWYH9XtxR5gAIDyQACC4Vb/niQv/O93ffyP/pE6/Cgq7HRsUM3g6gAAjoguMBhqz8lUeezjHWKxiAyLDpNHujXgOwIAqHAEIBjm5PlMuX/xVrmUa5KbGwXJiwObi4vLtWeAAQBQXghAMERaVq7cv2irnE3PlshgP5l7T1vxcOOfIwDAOviNA6vLNZnl0SXbJS7polT385IFMdHi7+3BdwIAYDUEIFiVxWKRp7/YKxsOJUslDzdZMCJaalepxHcBAGBVBCBY1bx1h2X5thOiFnt++6420iI0gO8AAMDqCECwmq92nZbXvj+gj6ff1lR6Nw3m6gMADEEAglVsO5Yikz/dpY9jOofLyM71uPIAAMMQgFDhjiVnyIP/2SY5eWbp0zRYnr61KVcdAGAoAhAq1PmMHIlZtFXOZ+ZKy9AAeXNYa7azAAAYjgCECpOVa5KHPtwmR5Mz9Eyv90dEiY8nu68AAIxHAEKFMJst8o/PdsvWY+fFz8tdFsZE6w1NAQCwBQQgVIhZq+P0rC93Vxd55952EhHsx5UGANgMAhDK3SdbT8icHw/p45cHtZAujYK4ygAAm0IAQrnacDBZnvxijz4e06OBDIkO4woDAGwOAQjlJi4pXR5ZEit5ZosMaFVLJvWJ5OoCAGwSAQjl4kx6lsQs3Crp2XkSVbeqvHZnS3FV+10AAGCDCEC4YZk5eTJq8TY5deGShFfzkfeGR4m3hxtXFgBgswhAuCEms0UeX7ZTdp9Mlao+HrIwpr0E+npyVQEANs3wADR37lwJDw8Xb29v6dChg2zZsuWaz+3evbu4uLgUud16662Fnrdv3z65/fbbJSAgQHx9fSU6Olri4+Ot8NU4n5e+2Serf08STzdX3fJTL8jX6JIAALDtALR8+XKZOHGiPPvss7J9+3Zp1aqV9OvXT86cOVPs81esWCEJCQkFt71794qbm5sMHjy44DmHDx+WLl26SOPGjWXdunWye/dueeaZZ3TAQvlavPGYLPjlqD6eOaSVRIcHcokBAHbBxWKxWIz65KrFR7XOzJkzR983m80SFhYm48aNk6lTp/7l62fPni3Tp0/XYUi19CjDhg0TDw8P+fDDD0tcR3Z2tr7lS0tL03WkpqaKv79/mb42R7d2X5Le4NRsEZnSL1LG9GhodEkAACeXlpame39K8vvbsBagnJwciY2Nld69e/9ZjKurvr9p06YSvccHH3ygA09++FEB6ptvvpGIiAjdklSjRg0dsr788svrvs+MGTP0Bcu/qfCDa9t7KlXGLt2hw8/QqDB5tHsDLhcAwK4YFoCSk5PFZDJJcHBwofPqfmJi4l++Xo0VUl1go0aNKjinus4uXrwor7zyivTv319WrVolgwYNkr///e/y008/XfO9pk2bptNi/u3EiRM3+NU5rtMXLsn9i7bKpVyTdGkYJP8c1FyPwwIAwJ7Y7dbcqvWnRYsW0r59+4JzqgVIueOOO2TChAn6uHXr1rJx40aZP3++dOvWrdj38vLy0jdcX3pWrg4/Z9KzJSK4ssy7t614uBk+jh4AgFIz7LdXUFCQHsCclJRU6Ly6HxISct3XZmRkyLJly+SBBx4o8p7u7u7StGnTQuebNGnCLLAblGsyy6MfbZf9ielS3c9LFoyMFn9vjxt9WwAAnCsAeXp6Srt27WTt2rWFWnDU/Y4dO173tZ9++qketHzvvfcWeU81qPrAgQOFzsfFxUndunXL+StwHmqc/PT/7pWfDyZLJQ83+WBElIRW9TG6LAAA7LMLTE2BHzFihERFRemuLDWrS7XuxMTE6MeHDx8utWvX1oOUr+7+GjhwoFSrVq3Ie06ZMkWGDh0qXbt2lR49esjKlSvl66+/1lPiUTbzfzoiH285IWqoz5vDWkvL0CpcSgCAXTM0AKmgcvbsWT2VXQ18VuN1VGDJHxitFi9UM8OupFp3NmzYoAc4F0cNelbjfVRoeuyxxyQyMlI+//xzvTYQSu9/u0/Lqyv36+PptzWVvs2u3z0JAIA9MHQdIEdYR8CRxR5Pkbv+vVly8swyslO4PHd7M6NLAgDAvtcBgm07fi5DHvxPrA4/vZsEyzO3FR5YDgCAPSMAoYjzGTkSs3CrpGTkSIvaAfLWXa3FzZW1fgAAjoMAhEKy80zy8IexciQ5Q2pXqaRnfPl42u1yUQAAFIsAhAJqONg/PtstW46liJ+Xu17rp4Y/m8gCABwPAQgF3lgdJ//deVrcXV30Ks+RIX5cHQCAQyIAQft02wl564dD+vilQc3l5kbVuTIAAIdFAIJsPJQs01bs0VdC7ew+NLoOVwUA4NAIQE7uYFK6PLwkVvLMFrmtZU2Z3DfS6JIAAKhwBCAndjY9W2IWbZX0rDyJqltVZg5uJa5MdwcAOAECkJO6lGOSUYu3ysnzlyS8mo+8NzxKvD3cjC4LAACrIAA5IZPZIuOX75BdJ1Olio+HLIxpL4G+nkaXBQCA1RCAnNCMb/fJ978liaebq/x7eJTUC/I1uiQAAKyKAORkPtx0TN7fcFQfvza4pUSHBxpdEgAAVkcAciI/7E+SZ7/6TR9P7hshd7SubXRJAAAYggDkJPaeSpWxS3eI2SIyJCpUxvRoaHRJAAAYhgDkBBJSL8kDi7dKZo5JOjesJi8NaiEuLuzuDgBwXgQgB5eelSsxC7dKUlq2NKpRWebd00483Pi2AwCcG78JHVieyay7vfYnpktQZS9ZGBMtAZU8jC4LAADDEYAclMVikelf/SY/xZ0Vbw9X+WBElIRW9TG6LAAAbAIByEG9t/6ILN0cL2qoz1vD2kirsCpGlwQAgM0gADmgb/ckyIzv9uvjp29tKn2bhRhdEgAANoUA5GC2x5+XCct36uMRHevK/Z3DjS4JAACbQwByIPHnMuXBxdskO88svRrXkOkDmjHdHQCAYhCAHMSFzBwZuWiLnMvIkWa1/OWtu9qImytr/QAAUBwCkAPIzjPJwx/GypGzGVIrwFsWjIwWXy93o8sCAMBmEYAcYLr7tM/3yOajKVLZy10WxERLsL+30WUBAGDTCEB2bvaag7Jixynd3TXvnrbSOMTf6JIAALB5BCA79nnsSXlz7UF9/M+BzaVrRHWjSwIAwC4QgOzUpsPnZOqK3fp4dLcGclf7OkaXBACA3SAA2aFDZ9Ll4Q+3Sa7JIre2rCn/6BdpdEkAANgVApCdSb6YLTGLtkpaVp60rVNFXh/cSlyZ7g4AQKkQgOxIVq5JRi3eJidSLkmdQB/59/Ao8fZwM7osAADsDgHITpjNFr3Fxc4TFySgkocsjImWapW9jC4LAAC7RACyE6+s3C/f7U0UTzdXee++dtKgemWjSwIAwG4RgOzAkl+Py3vrj+jjf93ZUjrUr2Z0SQAA2DUCkI378cAZmf7fvfp4Yp8IGdimttElAQBg9whANuz302ky9qPtYraI3NkuVMb1bGh0SQAAOAQCkI1KSL0k9y/aKhk5JunUoJq8PKiFuLiwuzsAAOWBAGSDLmbnyf2LtkliWpY0rFFZ3rm3nXi6860CAKC88FvVxuSZzDJ26XbZl5AmQZU9ZeHIaD3tHQAAlB8CkA2xWCzy3Ne/yboDZ8Xbw1XeHxEtYYE+RpcFAIDDIQDZkPd/PipLfo0XNdRn9tA20jqsitElAQDgkAhANuK7PQny8nf79PFTtzSR/s1DjC4JAACHRQCyATviz8v45TvFYhG576a68kCXekaXBACAQyMAGexESqbe4DQ7zyw9IqvLswOaMt0dAIAKRgAyUGpmroxcuEXOZeRI05r+MufutuLuxrcEAICK5l7hnwEFTGaLbDmaImfSsyTQx1Pm/nhIDp/NkBB/b1kwMlp8vfh2AABgDfzGtZKVexPk+a9/l4TUrELnvdxddfgJCfC2VikAADg9+lusFH4eWbK9SPhR1Nif+JQMp/+HCACANRGArNDtpVp+LNd4XO3upR5XzwMAANZBAKpgasxPcS0/+VTsUY+r5wEAAOsgAFUwNeC5PJ8HAABuHAGogtXw8y7X5wEAgBtHAKpg7esFSs0Abz3WpzjqvHpcPQ8AAFgHAaiCubm66NWdlatDUP599bh6HgAAsA4CkBX0b15T3rm3bZG1ftR9dV49DgAArIeFEK1EhZw+TUMKVoJWY35UtxctPwAAWB8ByIpU2OnYoJo1PyUAACgGXWAAAMDpEIAAAIDTIQABAACnQwACAABOxyYC0Ny5cyU8PFy8vb2lQ4cOsmXLlms+t3v37uLi4lLkduuttxb7/NGjR+vHZ8+eXYFfAQAAsCeGB6Dly5fLxIkT5dlnn5Xt27dLq1atpF+/fnLmzJlin79ixQpJSEgouO3du1fc3Nxk8ODBRZ77xRdfyK+//iq1atWywlcCAADsheEBaNasWfLggw9KTEyMNG3aVObPny8+Pj6yYMGCYp8fGBgoISEhBbfVq1fr518dgE6dOiXjxo2Tjz76SDw8PKz01QAAAHtgaADKycmR2NhY6d27958Fubrq+5s2bSrRe3zwwQcybNgw8fX1LThnNpvlvvvukylTpkizZs3+8j2ys7MlLS2t0A0AADguQwNQcnKymEwmCQ4OLnRe3U9MTPzL16uxQqoLbNSoUYXOv/rqq+Lu7i6PPfZYieqYMWOGBAQEFNzCwsJK+ZUAAAB7YtcrQavWnxYtWkj79u0LzqkWpTfffFOPJ1KDn0ti2rRpehxSvtTUVKlTpw4tQQAA2JH8HhyLxWLbASgoKEgPYE5KSip0Xt1X43uuJyMjQ5YtWyYvvPBCofM///yzHkCtAkw+1co0adIkPRPs2LFjRd7Ly8tL366+gLQEAQBgf9LT03WPjs0GIE9PT2nXrp2sXbtWBg4cWDB+R90fO3bsdV/76aef6rE79957b6HzauzPlWOKFDWrTJ1XA61LQs0aO3HihPj5+ZW4Fak0oqOjZevWrTb1fmV9j7K8rjSvUWFUBVH1/fD39y91fc6ovP99OUPNFf35HelnvqyvLelr+Jm3z58hW6lXtfyo8FOS2d+Gd4GprqcRI0ZIVFSU7spSrTSqdSc/rAwfPlxq166tx+lc3f2lQlO1aoU3F1X3rz6nZoGpFqXIyMgS1aQGYoeGhkpFUa1e5fnLvDzer6zvUZbXleU16vkEoIq7vs5ec0V/fkf6mS/ra0v7Gn7mK/574qj1/lXLj80EoKFDh8rZs2dl+vTpeuBz69atZeXKlQUDo+Pj43UgudKBAwdkw4YNsmrVKrFHY8aMsbn3K+t7lOV15f31w/6vr9E1V/Tnd6Sf+bK+1ujvsaOzt+s7xgbqdbGUZKQQYBDVHK7SvBqYbk9/3QAoG37m4TQLIQLXowanq1XCrxykDsBx8TMPa6EFCAAAOB1agAAAgNMhAAEAAKdDAAIAAE6HAAQAAJwOAQgAADgdAhDs1qBBg6Rq1apy5513Gl0KACtQW+J0795dmjZtKi1bttRbIgFlxTR42K1169bpPV8WL14sn332mdHlAKhgCQkJerNstWOA2jlA7SUZFxcnvr6+XHuUGi1AsFvqL0G1YS0A51CzZk0dfhS1v2NQUJCkpKQYXRbsFAEIhli/fr0MGDBA79jr4uIiX375ZZHnzJ07V8LDw8Xb21s6dOggW7ZsMaRWALb3cx8bGysmk0nCwsL49qBMCEAwREZGhrRq1Ur/Z1ec5cuXy8SJE/U2GNu3b9fP7devn5w5c8bqtQKwrZ971eozfPhwee+99/jWoMwYAwTDqb8Ev/jiCxk4cGDBOfWXX3R0tMyZM0ffN5vN+i+9cePGydSpUwuNA1LPYQwQ4Bw/99nZ2dKnTx958MEH5b777jOsftg/WoBgc3JycnTzdu/evQvOubq66vubNm0ytDYAxv3cWywWGTlypPTs2ZPwgxtGAILNSU5O1n37wcHBhc6r+2rmRz71H+PgwYPl22+/ldDQUMIR4OA/97/88ovuJlNjh9RgaHXbs2ePQRXD3rkbXQBQVmvWrOHiAU6kS5cuulsMKA+0AMHmqKmtbm5uer2PK6n7auorAMfDzz2sjQAEm+Pp6akXOFu7dm3BOfVXn7rfsWNHQ2sDUDH4uYe10QUGQ1y8eFEOHTpUcP/o0aOyc+dOCQwMlDp16uipsCNGjJCoqChp3769zJ49W0+hjYmJ4TsG2Cl+7mFLmAYPQ6jp6z169ChyXoWeRYsW6WM1Ffa1117TAyDVYMe33npLT5MFYJ/4uYctIQABAACnwxggAADgdAhAAADA6RCAAACA0yEAAQAAp0MAAgAATocABAAAnA4BCAAAOB0CEAAAcDoEIAAA4HQIQAAcTnh4uN4/DgCuhQAEwG6pfeOqVKlS5PzWrVvloYceqvDPT9AC7Be7wQNwONWrVxd7kpOTI56enkaXATgVWoAA3LDu3bvLY489Jv/4xz8kMDBQQkJC5LnnnivRay9cuCCjRo3SocXf31969uwpu3btKnhcHffo0UP8/Pz04+3atZNt27bpncVjYmIkNTVVXFxc9C3/c17dMqMee/fdd+W2224THx8fadKkiWzatEkOHTqka/f19ZVOnTrJ4cOHC16jju+44w4JDg6WypUrS3R0tKxZs6bQ13z8+HGZMGFCwefP9/nnn0uzZs3Ey8tL1/L6668X+prVuRdffFGGDx+uvybVWqVC0NixY6VmzZri7e0tdevWlRkzZpTxOwLgrxCAAJSLxYsX6yCxefNm+de//iUvvPCCrF69+i9fN3jwYDlz5ox89913EhsbK23btpVevXpJSkqKfvyee+6R0NBQ3a2lHp86dap4eHjowKJCjgoQCQkJ+jZ58uRrfp78wLFz505p3Lix3H333fLwww/LtGnTdKCyWCw6gOS7ePGi3HLLLbJ27VrZsWOH9O/fXwYMGCDx8fH68RUrVui61NeZ//kVVeOQIUNk2LBhsmfPHh3KnnnmGd1dd6WZM2dKq1at9Hurx9966y356quv5JNPPpEDBw7IRx99pIMSgApiAYAb1K1bN0uXLl0KnYuOjrY88cQT133dzz//bPH397dkZWUVOt+gQQPLu+++q4/9/PwsixYtKvb1CxcutAQEBBQ5X7duXcsbb7xRcF/9V/f0008X3N+0aZM+98EHHxSc+/jjjy3e3t7XrbdZs2aWt99++5qfR7n77rstffr0KXRuypQplqZNmxZ63cCBAws9Z9y4cZaePXtazGbzdWsAUD5oAQJQLlq2bFnovurKUS0716O6t1RLS7Vq1XQ3U/7t6NGjBd1REydO1F1kvXv3lldeeaVQN1VZ61PdWkqLFi0KncvKypK0tDR9X9WlWpRUd5kaaK3q2rdvX0EL0LWo53Tu3LnQOXX/4MGDYjKZCs5FRUUVes7IkSN161RkZKTuTly1alWZvk4AJcMgaADlQnVLXUmNiTGbzdd9jQoZKiip8TxXy5/dpbqQVHfVN998o7vJnn32WVm2bJkMGjSozPXlj9cp7lx+zSr8qC481VXVsGFDqVSpktx55516rE55UN2FV1Jdfyr4qa9RjTVS3Wgq9H322Wfl8vkAFEYAAmAY9Us/MTFR3N3drzveJSIiQt/UgOO77rpLFi5cqAOQmjl1ZatKefrll190q0x+0FJh7dixY4WeU9znVy1G6rVXv5eq383N7bqfU41nGjp0qL6psKXGHamxUGpgOYDyRRcYAMOoFo6OHTvKwIEDdZePChgbN26Up556Sg9MvnTpkh6YrFqI1IwrFSTUYGgVMhQVmlQwUQOVk5OTJTMzs9xqa9SokR7orLqlVFedaoW6ukVLff7169fLqVOn9OdXJk2apOtRg67j4uL04PA5c+Zcd4C2MmvWLPn4449l//79+nWffvqpnk1X3DpHAG4cAQiAYVS307fffitdu3bVU9pVK4maPaXCjhqTo1pMzp07p2dvqcdUt9Df/vY3ef755/Xr1Uyw0aNH6xYTNY1ezT4rLyqQVK1aVX8ONfurX79+usXqSmoGmAptDRo0KFh7SD1HzeRS3XTNmzeX6dOn6+ep1qTrUdP8Vf1qbJCacq/eV10bV1f+mwYqgosaCV0h7wwAAGCj+NMCAAA4HQIQgAqjFvO7cnr7lTe1UjIAGIUuMAAVJj09XZKSkop9TE1BV9s9AIARCEAAAMDp0AUGAACcDgEIAAA4HQIQAABwOgQgAADgdAhAAADA6RCAAACA0yEAAQAAcTb/D9xfPrNPnaAWAAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_list = [5, 10, 25, 50, 100, 200, 300]\n",
"oob = []\n",
"for n in n_list:\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\", UserWarning) # \"Some inputs do not have OOB scores\"\n",
" m = RandomForestClassifier(n_estimators=n, oob_score=True, random_state=42, n_jobs=-1)\n",
" m.fit(X_train, y_train)\n",
" oob.append(m.oob_score_)\n",
"plt.plot(n_list, oob, \"o-\"); plt.xscale(\"log\")\n",
"plt.xlabel(\"n_estimators\"); plt.ylabel(\"OOB accuracy\"); plt.show()"
]
},
{
"cell_type": "markdown",
"id": "69e7913c",
"metadata": {},
"source": [
"## 7. 特徵重要性：不純度 vs 排列\n",
"\n",
"我們故意加一欄**純雜訊** `random_id`（0–999 的隨機整數，跟心臟病毫無關係），看兩種重要性怎麼排它。\n",
"為了不被單一測試集的運氣左右，用 5 折交叉驗證，每折都在「沒參與訓練」的那一折上算排列重要性，最後取平均。"
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" impurity | \n",
" permutation | \n",
" rank_impurity | \n",
" rank_permutation | \n",
"
\n",
" \n",
" \n",
" \n",
" | thal | \n",
" 0.117 | \n",
" 0.037 | \n",
" 1 | \n",
" 2 | \n",
"
\n",
" \n",
" | ca | \n",
" 0.116 | \n",
" 0.050 | \n",
" 2 | \n",
" 1 | \n",
"
\n",
" \n",
" | cp | \n",
" 0.114 | \n",
" 0.019 | \n",
" 3 | \n",
" 3 | \n",
"
\n",
" \n",
" | thalach | \n",
" 0.109 | \n",
" 0.005 | \n",
" 4 | \n",
" 8 | \n",
"
\n",
" \n",
" | oldpeak | \n",
" 0.104 | \n",
" 0.017 | \n",
" 5 | \n",
" 4 | \n",
"
\n",
" \n",
" | random_id | \n",
" 0.083 | \n",
" -0.003 | \n",
" 6 | \n",
" 13 | \n",
"
\n",
" \n",
" | age | \n",
" 0.080 | \n",
" -0.002 | \n",
" 7 | \n",
" 12 | \n",
"
\n",
" \n",
" | chol | \n",
" 0.067 | \n",
" 0.005 | \n",
" 8 | \n",
" 9 | \n",
"
\n",
" \n",
" | trestbps | \n",
" 0.062 | \n",
" 0.006 | \n",
" 9 | \n",
" 7 | \n",
"
\n",
" \n",
" | exang | \n",
" 0.046 | \n",
" 0.014 | \n",
" 10 | \n",
" 5 | \n",
"
\n",
" \n",
" | slope | \n",
" 0.044 | \n",
" 0.002 | \n",
" 11 | \n",
" 11 | \n",
"
\n",
" \n",
" | sex | \n",
" 0.030 | \n",
" 0.014 | \n",
" 12 | \n",
" 6 | \n",
"
\n",
" \n",
" | restecg | \n",
" 0.019 | \n",
" -0.005 | \n",
" 13 | \n",
" 14 | \n",
"
\n",
" \n",
" | fbs | \n",
" 0.009 | \n",
" 0.002 | \n",
" 14 | \n",
" 10 | \n",
"
\n",
" \n",
"
\n",
"