{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a9f26f4a",
   "metadata": {},
   "source": [
    "# 第 11 章　各種模型使用時機比較與效能提升策略\n",
    "\n",
    "這是「醫學生的機器學習入門」第 11 章的配套 Notebook。可以直接在 Google Colab 執行（上方選單「執行階段 → 全部執行」），不需要另外安裝套件。\n",
    "\n",
    "- 網站章節：`docs/chapters/11-model-selection.md`（網站上的「第 11 章」）\n",
    "- 資料集：Breast Cancer Wisconsin Diagnostic（WDBC，scikit-learn 內建）、CDC Diabetes Health Indicators（UCI id 891，原始資料 CC0）\n",
    "- 圖上的標籤用英文，是因為 Colab 預設沒有中文字型。\n",
    "- **本例僅供學習，不構成臨床建議。**CDC 資料是自陳問卷，不是診斷資料。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb35007",
   "metadata": {},
   "source": [
    "第一步：匯入套件並印出版本。本 Notebook 以 Colab 的 scikit-learn 1.6.1 為相容底線，較新版本也能跑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4edd4f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:34.460522Z",
     "iopub.status.busy": "2026-09-29T20:05:34.460139Z",
     "iopub.status.idle": "2026-09-29T20:05:35.357093Z",
     "shell.execute_reply": "2026-09-29T20:05:35.356722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy 2.1.3 | pandas 2.2.3 | scikit-learn 1.6.1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import (train_test_split, cross_val_score, StratifiedKFold,\n",
    "                                     KFold, GroupKFold, GridSearchCV, RandomizedSearchCV,\n",
    "                                     validation_curve)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import (confusion_matrix, roc_auc_score, roc_curve, average_precision_score,\n",
    "                             precision_recall_curve, brier_score_loss, balanced_accuracy_score)\n",
    "from sklearn.calibration import calibration_curve\n",
    "\n",
    "print(\"numpy\", np.__version__, \"| pandas\", pd.__version__, \"| scikit-learn\", sklearn.__version__)\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9738edf",
   "metadata": {},
   "source": [
    "## 1. 同一份資料、7 種模型：用交叉驗證公平比較\n",
    "\n",
    "先用平衡的 WDBC（乳房腫塊細針抽吸的影像特徵，良性 vs 惡性）。每個模型都跑 **10 折分層交叉驗證（Stratified K-fold）**，報告 AUC 的平均 ± 標準差。需要標準化的模型（邏輯迴歸、SVM、神經網路）把 `StandardScaler` 放進 Pipeline，讓每一折只用訓練折來算平均與標準差，避免資料洩漏。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad51a108",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:35.358297Z",
     "iopub.status.busy": "2026-09-29T20:05:35.358204Z",
     "iopub.status.idle": "2026-09-29T20:05:43.055206Z",
     "shell.execute_reply": "2026-09-29T20:05:43.054199Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              mean_AUC     SD\n",
      "DecisionTree     0.920  0.024\n",
      "NaiveBayes       0.989  0.008\n",
      "RandomForest     0.991  0.012\n",
      "MLP              0.992  0.015\n",
      "HistGB           0.992  0.011\n",
      "LogReg           0.995  0.007\n",
      "SVM (RBF)        0.996  0.005\n"
     ]
    }
   ],
   "source": [
    "X, y = load_breast_cancer(return_X_y=True, as_frame=True)\n",
    "models = {\n",
    "    \"LogReg\": make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000)),\n",
    "    \"NaiveBayes\": GaussianNB(),\n",
    "    \"SVM (RBF)\": make_pipeline(StandardScaler(), SVC()),\n",
    "    \"DecisionTree\": DecisionTreeClassifier(random_state=SEED),\n",
    "    \"RandomForest\": RandomForestClassifier(n_estimators=200, random_state=SEED),\n",
    "    \"MLP\": make_pipeline(StandardScaler(),\n",
    "                         MLPClassifier(hidden_layer_sizes=(16, 8), max_iter=1000, random_state=SEED)),\n",
    "    \"HistGB\": HistGradientBoostingClassifier(random_state=SEED),\n",
    "}\n",
    "cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=SEED)\n",
    "scores = {name: cross_val_score(m, X, y, cv=cv, scoring=\"roc_auc\") for name, m in models.items()}\n",
    "summary = pd.DataFrame({\"mean_AUC\": {k: v.mean() for k, v in scores.items()},\n",
    "                        \"SD\": {k: v.std() for k, v in scores.items()}}).sort_values(\"mean_AUC\")\n",
    "print(summary.round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a901d2d9",
   "metadata": {},
   "source": [
    "輸出是每個模型 10 折 AUC 的平均與標準差。注意前幾名的差距（約 0.00x）比標準差（約 0.01）還小，所以不能說誰「最好」，只能說它們在這份資料上表現相近；決策樹則明顯落後。下面把每一折畫出來。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cdf891d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:43.057334Z",
     "iopub.status.busy": "2026-09-29T20:05:43.057171Z",
     "iopub.status.idle": "2026-09-29T20:05:43.165417Z",
     "shell.execute_reply": "2026-09-29T20:05:43.164697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "for i, name in enumerate(summary.index):\n",
    "    s = scores[name]\n",
    "    ax.scatter(s, np.full(len(s), i), color=\"#607D8B\", alpha=0.5, s=15)\n",
    "    ax.errorbar(s.mean(), i, xerr=s.std(), fmt=\"o\", color=\"#00897B\", capsize=4)\n",
    "ax.set_yticks(range(len(summary)), summary.index)\n",
    "ax.set_xlabel(\"AUC (10-fold stratified CV; grey = each fold, green = mean +/- SD)\")\n",
    "ax.set_title(\"WDBC: cross-validated AUC of 7 models\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b88ea5bb",
   "metadata": {},
   "source": [
    "## 2. 三種切法：K-fold、Stratified K-fold、Group K-fold\n",
    "\n",
    "用一個只有 10% 陽性的玩具資料看差別：普通 K-fold 每一折的陽性比例會亂跳，分層（stratified）版本則每折都維持約 10%。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e7cab0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:43.167654Z",
     "iopub.status.busy": "2026-09-29T20:05:43.167492Z",
     "iopub.status.idle": "2026-09-29T20:05:43.172302Z",
     "shell.execute_reply": "2026-09-29T20:05:43.171525Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KFold            positive rate in each test fold: [0.1 0.1 0.2 0.  0.1]\n",
      "StratifiedKFold  positive rate in each test fold: [0.1 0.1 0.1 0.1 0.1]\n"
     ]
    }
   ],
   "source": [
    "y_toy = np.array([1] * 10 + [0] * 90)\n",
    "X_toy = np.zeros((100, 1))\n",
    "for name, splitter in [(\"KFold\", KFold(5, shuffle=True, random_state=SEED)),\n",
    "                       (\"StratifiedKFold\", StratifiedKFold(5, shuffle=True, random_state=SEED))]:\n",
    "    rates = [y_toy[test].mean() for _, test in splitter.split(X_toy, y_toy)]\n",
    "    print(f\"{name:16s} positive rate in each test fold:\", np.round(rates, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83438f62",
   "metadata": {},
   "source": [
    "接著是 **Group K-fold**：假設 20 位病人、每人 5 筆回診紀錄。用 GroupKFold 時，同一位病人的紀錄只會同時出現在訓練或測試其中一邊。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "396587e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:43.174221Z",
     "iopub.status.busy": "2026-09-29T20:05:43.174079Z",
     "iopub.status.idle": "2026-09-29T20:05:43.178036Z",
     "shell.execute_reply": "2026-09-29T20:05:43.177266Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fold 0: test patients [4, 9, 14, 19], overlap with train = 0\n",
      "fold 1: test patients [5, 10, 15, 18], overlap with train = 0\n",
      "fold 2: test patients [1, 6, 11, 16], overlap with train = 0\n",
      "fold 3: test patients [2, 7, 12, 17], overlap with train = 0\n",
      "fold 4: test patients [0, 3, 8, 13], overlap with train = 0\n"
     ]
    }
   ],
   "source": [
    "patient_id = np.repeat(np.arange(20), 5)          # 20 patients x 5 visits\n",
    "X_visits = np.zeros((100, 1))\n",
    "for fold, (tr, te) in enumerate(GroupKFold(n_splits=5).split(X_visits, groups=patient_id)):\n",
    "    overlap = set(patient_id[tr]) & set(patient_id[te])\n",
    "    print(f\"fold {fold}: test patients {sorted(int(p) for p in set(patient_id[te]))}, overlap with train = {len(overlap)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b83ede66",
   "metadata": {},
   "source": [
    "每一折的重疊病人數都是 0。如果改用普通 KFold，同一位病人的回診會同時出現在兩邊，模型等於「看過考題」，分數會虛高。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25c44491",
   "metadata": {},
   "source": [
    "## 3. 超參數搜尋：GridSearchCV 與 RandomizedSearchCV\n",
    "\n",
    "先留一份測試集完全不碰，只在訓練集內用交叉驗證挑 SVM 的 `C` 與 `gamma`。Pipeline 內的參數名稱寫成「步驟名__參數名」。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "09c62e09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:43.179844Z",
     "iopub.status.busy": "2026-09-29T20:05:43.179744Z",
     "iopub.status.idle": "2026-09-29T20:05:43.557927Z",
     "shell.execute_reply": "2026-09-29T20:05:43.557529Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params: {'svc__C': 10, 'svc__gamma': 0.01}\n",
      "best CV AUC (inside training set): 0.997\n",
      "AUC on untouched test set: 0.999\n"
     ]
    }
   ],
   "source": [
    "X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.25, stratify=y, random_state=SEED)\n",
    "svm = make_pipeline(StandardScaler(), SVC())\n",
    "grid = GridSearchCV(svm, {\"svc__C\": [0.1, 1, 10, 100], \"svc__gamma\": [0.001, 0.01, 0.1, 1]},\n",
    "                    cv=StratifiedKFold(5, shuffle=True, random_state=SEED), scoring=\"roc_auc\")\n",
    "grid.fit(X_tr, y_tr)\n",
    "print(\"best params:\", grid.best_params_)\n",
    "print(\"best CV AUC (inside training set):\", round(grid.best_score_, 3))\n",
    "print(\"AUC on untouched test set:\", round(roc_auc_score(y_te, grid.decision_function(X_te)), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01925dc",
   "metadata": {},
   "source": [
    "`best_score_` 是在訓練集內挑出來的最佳分數，帶有「挑選過」的樂觀偏差；真正要報告的是最後一行，在從未參與挑選的測試集上的表現。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e5326f0",
   "metadata": {},
   "source": [
    "參數組合太多時改用隨機搜尋：給每個參數一個分布，只抽 `n_iter` 組來試。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ed9f8c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:43.559737Z",
     "iopub.status.busy": "2026-09-29T20:05:43.559560Z",
     "iopub.status.idle": "2026-09-29T20:05:48.518699Z",
     "shell.execute_reply": "2026-09-29T20:05:48.518225Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best params: {'max_depth': 5, 'min_samples_leaf': 7, 'n_estimators': 124}\n",
      "best CV AUC: 0.989 | test AUC: 0.993\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import randint\n",
    "rand = RandomizedSearchCV(RandomForestClassifier(random_state=SEED),\n",
    "                          {\"n_estimators\": randint(50, 300), \"max_depth\": [None, 3, 5, 8],\n",
    "                           \"min_samples_leaf\": randint(1, 10)},\n",
    "                          n_iter=10, cv=StratifiedKFold(5, shuffle=True, random_state=SEED),\n",
    "                          scoring=\"roc_auc\", random_state=SEED)\n",
    "rand.fit(X_tr, y_tr)\n",
    "print(\"best params:\", rand.best_params_)\n",
    "print(\"best CV AUC:\", round(rand.best_score_, 3),\n",
    "      \"| test AUC:\", round(roc_auc_score(y_te, rand.predict_proba(X_te)[:, 1]), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b6b864",
   "metadata": {},
   "source": [
    "## 4. 不平衡資料：CDC 糖尿病指標\n",
    "\n",
    "CDC Diabetes Health Indicators 來自 2015 年美國 BRFSS 電話問卷，約 25 萬人、21 個特徵，糖尿病（含前期）比例約 13.9%。全部下載約需 20–40 秒；為了讓 Notebook 跑得快，我們分層抽樣 2 萬人。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "263fb9b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:05:48.519987Z",
     "iopub.status.busy": "2026-09-29T20:05:48.519912Z",
     "iopub.status.idle": "2026-09-29T20:06:06.526817Z",
     "shell.execute_reply": "2026-09-29T20:06:06.526370Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subsample: (20000, 21) | prevalence: 0.139\n",
      "train: (15000, 21) | test: (5000, 21)\n"
     ]
    }
   ],
   "source": [
    "CDC_URL = \"https://archive.ics.uci.edu/static/public/891/data.csv\"\n",
    "try:\n",
    "    cdc = pd.read_csv(CDC_URL)\n",
    "except Exception as e:\n",
    "    raise RuntimeError(\"無法下載 CDC 資料，請確認網路後重試；或改用 ucimlrepo：\"\n",
    "                       \"from ucimlrepo import fetch_ucirepo; fetch_ucirepo(id=891)\") from e\n",
    "cdc = cdc.drop(columns=[\"ID\"], errors=\"ignore\")\n",
    "cdc_sub, _ = train_test_split(cdc, train_size=20000, stratify=cdc[\"Diabetes_binary\"], random_state=SEED)\n",
    "Xd, yd = cdc_sub.drop(columns=\"Diabetes_binary\"), cdc_sub[\"Diabetes_binary\"]\n",
    "Xd_tr, Xd_te, yd_tr, yd_te = train_test_split(Xd, yd, test_size=0.25, stratify=yd, random_state=SEED)\n",
    "print(\"subsample:\", Xd.shape, \"| prevalence:\", round(yd.mean(), 3))\n",
    "print(\"train:\", Xd_tr.shape, \"| test:\", Xd_te.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ef4406",
   "metadata": {},
   "source": [
    "輸出顯示抽樣後仍維持約 13.9% 的陽性比例。先看「準確率陷阱」：一個永遠說「沒有糖尿病」的模型，準確率有多高？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44440c03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:06.528041Z",
     "iopub.status.busy": "2026-09-29T20:06:06.527969Z",
     "iopub.status.idle": "2026-09-29T20:06:06.537492Z",
     "shell.execute_reply": "2026-09-29T20:06:06.537186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "always-negative      accuracy=0.861  sensitivity=0.000  balanced_acc=0.500\n",
      "logistic regression  accuracy=0.866  sensitivity=0.164  balanced_acc=0.572\n"
     ]
    }
   ],
   "source": [
    "dummy = DummyClassifier(strategy=\"most_frequent\").fit(Xd_tr, yd_tr)\n",
    "lr = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000)).fit(Xd_tr, yd_tr)\n",
    "for name, m in [(\"always-negative\", dummy), (\"logistic regression\", lr)]:\n",
    "    pred = m.predict(Xd_te)\n",
    "    tn, fp, fn, tp = confusion_matrix(yd_te, pred).ravel()\n",
    "    print(f\"{name:20s} accuracy={(tp + tn) / len(yd_te):.3f}  sensitivity={tp / (tp + fn):.3f}  \"\n",
    "          f\"balanced_acc={balanced_accuracy_score(yd_te, pred):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c6c116",
   "metadata": {},
   "source": [
    "永遠猜陰性就有約 86% 準確率，但敏感度是 0。邏輯迴歸在預設閾值 0.5 下準確率只略高一點，敏感度卻很低——問題不在模型，而在閾值。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641b4af6",
   "metadata": {},
   "source": [
    "## 5. 閾值、2×2 表與醫學指標\n",
    "\n",
    "寫一個小函式，把 2×2 表轉成醫學讀者熟悉的敏感度、特異度、PPV、NPV。規則：預測機率 ≥ 閾值就判陽性（網站互動 demo 用同一規則）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09fc0f96",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:06.538582Z",
     "iopub.status.busy": "2026-09-29T20:06:06.538508Z",
     "iopub.status.idle": "2026-09-29T20:06:06.544898Z",
     "shell.execute_reply": "2026-09-29T20:06:06.544505Z"
    }
   },
   "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>threshold</th>\n",
       "      <th>TP</th>\n",
       "      <th>FP</th>\n",
       "      <th>FN</th>\n",
       "      <th>TN</th>\n",
       "      <th>sensitivity</th>\n",
       "      <th>specificity</th>\n",
       "      <th>PPV</th>\n",
       "      <th>NPV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.14</td>\n",
       "      <td>550</td>\n",
       "      <td>1215</td>\n",
       "      <td>147</td>\n",
       "      <td>3088</td>\n",
       "      <td>0.789</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.312</td>\n",
       "      <td>0.955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.30</td>\n",
       "      <td>318</td>\n",
       "      <td>414</td>\n",
       "      <td>379</td>\n",
       "      <td>3889</td>\n",
       "      <td>0.456</td>\n",
       "      <td>0.904</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.50</td>\n",
       "      <td>114</td>\n",
       "      <td>87</td>\n",
       "      <td>583</td>\n",
       "      <td>4216</td>\n",
       "      <td>0.164</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.567</td>\n",
       "      <td>0.879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   threshold   TP    FP   FN    TN  sensitivity  specificity    PPV    NPV\n",
       "0       0.14  550  1215  147  3088        0.789        0.718  0.312  0.955\n",
       "1       0.30  318   414  379  3889        0.456        0.904  0.434  0.911\n",
       "2       0.50  114    87  583  4216        0.164        0.980  0.567  0.879"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def clinical_metrics(y_true, prob, t):\n",
    "    pred = (prob >= t).astype(int)\n",
    "    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()\n",
    "    return {\"threshold\": t, \"TP\": tp, \"FP\": fp, \"FN\": fn, \"TN\": tn,\n",
    "            \"sensitivity\": round(tp / (tp + fn), 3), \"specificity\": round(tn / (tn + fp), 3),\n",
    "            \"PPV\": round(tp / (tp + fp), 3) if tp + fp else np.nan, \"NPV\": round(tn / (tn + fn), 3)}\n",
    "\n",
    "p_lr = np.round(lr.predict_proba(Xd_te)[:, 1], 3)   # rounded like the website demo\n",
    "pd.DataFrame([clinical_metrics(yd_te, p_lr, t) for t in [0.14, 0.3, 0.5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3119d4b8",
   "metadata": {},
   "source": [
    "閾值從 0.5 降到 0.14（接近盛行率），敏感度大幅上升、特異度下降、PPV 也跟著降。這和網站上 ROC 滑桿 demo 同一閾值的數字應該一致。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b01b21f8",
   "metadata": {},
   "source": [
    "把所有閾值畫在一起，就是 ROC 曲線；不平衡時再加看 PR 曲線。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c048dd17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:06.545975Z",
     "iopub.status.busy": "2026-09-29T20:06:06.545910Z",
     "iopub.status.idle": "2026-09-29T20:06:06.949491Z",
     "shell.execute_reply": "2026-09-29T20:06:06.949002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgb = HistGradientBoostingClassifier(random_state=SEED).fit(Xd_tr, yd_tr)\n",
    "p_hgb = hgb.predict_proba(Xd_te)[:, 1]\n",
    "fig, (a1, a2) = plt.subplots(1, 2, figsize=(10, 4))\n",
    "for p, name in [(p_lr, \"LogReg\"), (p_hgb, \"HistGB\")]:\n",
    "    fpr, tpr, _ = roc_curve(yd_te, p)\n",
    "    a1.plot(fpr, tpr, label=f\"{name} AUC={roc_auc_score(yd_te, p):.3f}\")\n",
    "    prec, rec, _ = precision_recall_curve(yd_te, p)\n",
    "    a2.plot(rec, prec, label=f\"{name} AP={average_precision_score(yd_te, p):.3f}\")\n",
    "a1.plot([0, 1], [0, 1], \"--\", color=\"grey\")\n",
    "a2.axhline(yd_te.mean(), ls=\"--\", color=\"grey\", label=\"prevalence\")\n",
    "a1.set(xlabel=\"1 - specificity\", ylabel=\"sensitivity\", title=\"ROC\")\n",
    "a2.set(xlabel=\"sensitivity (recall)\", ylabel=\"PPV (precision)\", title=\"Precision-recall\")\n",
    "a1.legend(); a2.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccad6fde",
   "metadata": {},
   "source": [
    "兩個模型在單一測試集上的 AUC 很接近。要判斷差距是否穩定，應該在訓練集上做交叉驗證並看標準差："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f188a32e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:06.951030Z",
     "iopub.status.busy": "2026-09-29T20:06:06.950937Z",
     "iopub.status.idle": "2026-09-29T20:06:09.652875Z",
     "shell.execute_reply": "2026-09-29T20:06:09.652035Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg: CV AUC 0.819 +/- 0.010\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HistGB: CV AUC 0.816 +/- 0.006\n"
     ]
    }
   ],
   "source": [
    "cv5 = StratifiedKFold(5, shuffle=True, random_state=SEED)\n",
    "for name, m in [(\"LogReg\", make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))),\n",
    "                (\"HistGB\", HistGradientBoostingClassifier(random_state=SEED))]:\n",
    "    s = cross_val_score(m, Xd_tr, yd_tr, cv=cv5, scoring=\"roc_auc\")\n",
    "    print(f\"{name}: CV AUC {s.mean():.3f} +/- {s.std():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4265c048",
   "metadata": {},
   "source": [
    "兩者平均 AUC 的差距落在標準差範圍內，表示在這份資料上看不出誰穩定勝出；此時較簡單、可解釋的邏輯迴歸通常是合理選擇。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f6d0e22",
   "metadata": {},
   "source": [
    "## 6. class_weight 與校準\n",
    "\n",
    "`class_weight=\"balanced\"` 讓模型更重視少數類別。它會把預測機率整體往上推：排序能力（AUC）幾乎不變，但機率不再代表真實風險。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f307387b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:09.655636Z",
     "iopub.status.busy": "2026-09-29T20:06:09.655455Z",
     "iopub.status.idle": "2026-09-29T20:06:09.794001Z",
     "shell.execute_reply": "2026-09-29T20:06:09.793107Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC   plain: 0.827 | balanced: 0.828\n",
      "Brier plain: 0.098 | balanced: 0.177\n",
      "mean predicted risk plain: 0.14 | balanced: 0.388 | observed prevalence: 0.139\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_bal = make_pipeline(StandardScaler(),\n",
    "                       LogisticRegression(max_iter=1000, class_weight=\"balanced\")).fit(Xd_tr, yd_tr)\n",
    "p_bal = lr_bal.predict_proba(Xd_te)[:, 1]\n",
    "print(\"AUC   plain:\", round(roc_auc_score(yd_te, p_lr), 3), \"| balanced:\", round(roc_auc_score(yd_te, p_bal), 3))\n",
    "print(\"Brier plain:\", round(brier_score_loss(yd_te, p_lr), 3), \"| balanced:\", round(brier_score_loss(yd_te, p_bal), 3))\n",
    "print(\"mean predicted risk plain:\", round(p_lr.mean(), 3), \"| balanced:\", round(p_bal.mean(), 3),\n",
    "      \"| observed prevalence:\", round(yd_te.mean(), 3))\n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax.plot([0, 1], [0, 1], \"--\", color=\"grey\")\n",
    "for p, name in [(p_lr, \"plain\"), (p_bal, \"class_weight=balanced\")]:\n",
    "    frac, mean_p = calibration_curve(yd_te, p, n_bins=10, strategy=\"quantile\")\n",
    "    ax.plot(mean_p, frac, \"o-\", label=name)\n",
    "ax.set(xlabel=\"mean predicted probability\", ylabel=\"observed fraction positive\", title=\"Calibration\")\n",
    "ax.legend(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8c1c761",
   "metadata": {},
   "source": [
    "Brier 分數越低越好。加權版的平均預測風險遠高於實際盛行率（約 14%），校準曲線整條落在對角線下方：它適合「排序／篩選」，但不適合直接告訴病人「你的風險是 X%」。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49db2326",
   "metadata": {},
   "source": [
    "## 7. 偏差與變異：驗證曲線\n",
    "\n",
    "改變決策樹的最大深度，比較訓練分數與交叉驗證分數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf8493e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-09-29T20:06:09.796580Z",
     "iopub.status.busy": "2026-09-29T20:06:09.796430Z",
     "iopub.status.idle": "2026-09-29T20:06:11.285136Z",
     "shell.execute_reply": "2026-09-29T20:06:11.284724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best depth by CV: 5\n"
     ]
    }
   ],
   "source": [
    "depths = np.arange(1, 16)\n",
    "tr_s, va_s = validation_curve(DecisionTreeClassifier(random_state=SEED), Xd_tr, yd_tr,\n",
    "                              param_name=\"max_depth\", param_range=depths, cv=cv5, scoring=\"roc_auc\")\n",
    "plt.plot(depths, tr_s.mean(1), \"o-\", label=\"train\")\n",
    "plt.plot(depths, va_s.mean(1), \"o-\", label=\"cross-validation\")\n",
    "plt.xlabel(\"max_depth\"); plt.ylabel(\"AUC\"); plt.legend(); plt.title(\"Validation curve\")\n",
    "plt.show()\n",
    "print(\"best depth by CV:\", depths[va_s.mean(1).argmax()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "308e1a81",
   "metadata": {},
   "source": [
    "深度小時兩條線都低（偏差大、配適不足）；深度大時訓練分數接近 1、驗證分數卻下滑（變異大、過擬合）。交叉驗證分數最高的深度就是這兩者的平衡點。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8ca730",
   "metadata": {},
   "source": [
    "## 動手試試\n",
    "\n",
    "1. 在第 5 節把閾值改成 0.2、0.25，觀察敏感度與 PPV 如何交換。如果這是「篩檢後再做 HbA1c 確認」的情境，你會選哪個閾值？為什麼？\n",
    "2. 在第 1 節把 `StratifiedKFold` 的 `random_state` 改成 0 或 1 重跑，各模型的排名有沒有變？這告訴你單次分數差距的意義是什麼？\n",
    "3. 在第 3 節的 GridSearchCV 裡把 `scoring` 改成 `\"balanced_accuracy\"`，最佳參數會不會改變？"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
