Day_042_HW.ipynb

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    "## [作業重點]\n",
    "目前你應該已經要很清楚資料集中，資料的型態是什麼樣子囉！包含特徵 (features) 與標籤 (labels)。因此要記得未來不管什麼專案，必須要把資料清理成相同的格式，才能送進模型訓練。\n",
    "今天的作業開始踏入決策樹這個非常重要的模型，請務必確保你理解模型中每個超參數的意思，並試著調整看看，對最終預測結果的影響為何"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "\n",
    "1. 試著調整 DecisionTreeClassifier(...) 中的參數，並觀察是否會改變結果？\n",
    "2. 改用其他資料集 (boston, wine)，並與回歸模型的結果進行比較"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, metrics\n",
    "\n",
    "# 如果是分類問題，請使用 DecisionTreeClassifier，若為回歸問題，請使用 DecisionTreeRegressor\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取鳶尾花資料集\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.25, random_state=4)\n",
    "\n",
    "# 建立模型\n",
    "clf = DecisionTreeClassifier(criterion='gini',max_depth=2,min_samples_split=8)\n",
    "\n",
    "# 訓練模型\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "# 預測測試集\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy:  0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "acc = metrics.accuracy_score(y_test, y_pred)\n",
    "print(\"Acuuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance:  [0. 0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importance: \", clf.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = datasets.load_wine()\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.30, random_state=4)\n",
    "\n",
    "# # 建立模型\n",
    "clf = DecisionTreeClassifier(max_depth=2, min_samples_split=15)\n",
    "\n",
    "# # 訓練模型\n",
    "clf.fit(x_train, y_train)\n",
    "\n",
    "# 預測測試集\n",
    "y_pred = clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acuuracy:  0.9074074074074074\n"
     ]
    }
   ],
   "source": [
    "acc = metrics.accuracy_score(y_test, y_pred)\n",
    "print(\"Acuuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance:  [0.         0.         0.         0.         0.         0.\n",
      " 0.47451769 0.         0.         0.46889218 0.         0.\n",
      " 0.05659013]\n"
     ]
    }
   ],
   "source": [
    "print(\"Feature importance: \", clf.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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