Day_042_HW-決策樹 程式碼撰寫.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## [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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston, load_wine\n",
    "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取 Boston 資料\n",
    "boston = load_boston()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.1, random_state=4)\n",
    "\n",
    "# 建立一個線性回歸模型\n",
    "regr = DecisionTreeRegressor()\n",
    "\n",
    "# 將訓練資料丟進去模型訓練\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# 將測試資料丟進模型得到預測結果\n",
    "y_pred = regr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "Mean squared error: 26.28\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 讀取 wine 資料\n",
    "wine = load_wine()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.1, random_state=4)\n",
    "\n",
    "# 建立一個羅吉斯回歸模型\n",
    "regr = DecisionTreeClassifier()\n",
    "\n",
    "# 將訓練資料丟進去模型訓練\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# 將測試資料丟進模型得到預測結果\n",
    "y_pred = regr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019/10/16"
   ]
  }
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