Day_040_HW-LASSO, Ridge Regression 程式碼.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## [作業重點]\n",
    "使用 Sklearn 中的 Lasso, Ridge 模型，來訓練各種資料集，務必了解送進去模型訓練的**資料型態**為何，也請了解模型中各項參數的意義。\n",
    "\n",
    "機器學習的模型非常多種，但要訓練的資料多半有固定的格式，確保你了解訓練資料的格式為何，這樣在應用新模型時，就能夠最快的上手開始訓練！"
   ]
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    "## 練習時間\n",
    "試著使用 sklearn datasets 的其他資料集 (boston, ...)，來訓練自己的線性迴歸模型，並加上適當的正則化來觀察訓練情形。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston, load_wine\n",
    "from sklearn.linear_model import Lasso, Ridge\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 = Lasso()\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": {},
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     "name": "stdout",
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     "text": [
      "Mean squared error: 23.24\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 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 = Ridge()\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": [
      "Mean squared error: 17.35\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019/10/16"
   ]
  }
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