classic-machine-learning/ridge_regression_1.ipynb

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    "Copyright (C)\n",
    "2016 - 2019 Pinard Liu(liujianping-ok@163.com)\n",
    "\n",
    "https://www.cnblogs.com/pinard\n",
    "\n",
    "Permission given to modify the code as long as you keep this declaration at the top\n",
    "\n",
    "用scikit-learn和pandas学习Ridge回归 https://www.cnblogs.com/pinard/p/6023000.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "collapsed": true
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets, linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "source": [
    "# read_csv里面的参数是csv在你电脑上的路径，此处csv文件放在notebook运行目录下面的CCPP目录里\n",
    "data = pd.read_csv('.\\CCPP\\ccpp.csv')"
   ]
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     "text": [
      "C:\\Python27\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "X = data[['AT', 'V', 'AP', 'RH']]\n",
    "y = data[['PE']]\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
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     "text": [
      "0.927643364697\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge = Ridge(alpha=1)\n",
    "ridge.fit(X_train, y_train)\n",
    "print ridge.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "[[-1.97373209 -0.2323016   0.06935852 -0.15806479]]\n",
      "[ 447.05552892]\n",
      "MSE: 20.0804091931\n",
      "RMSE: 4.4811169582\n"
     ]
    }
   ],
   "source": [
    "print ridge.coef_\n",
    "print ridge.intercept_\n",
    "y_pred = ridge.predict(X_test)\n",
    "from sklearn import metrics\n",
    "# 用scikit-learn计算MSE\n",
    "print \"MSE:\",metrics.mean_squared_error(y_test, y_pred)\n",
    "# 用scikit-learn计算RMSE\n",
    "print \"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 28,
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       "7.0"
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     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "ridgecv = RidgeCV(alphas=[0.01, 0.1, 0.5, 1, 3, 5, 7, 10, 20, 100])\n",
    "ridgecv.fit(X_train, y_train)\n",
    "ridgecv.alpha_  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 20.0804574476\n",
      "RMSE: 4.48112234241\n"
     ]
    }
   ],
   "source": [
    "y_pred = ridgecv.predict(X_test)\n",
    "from sklearn import metrics\n",
    "# 用scikit-learn计算MSE\n",
    "print \"MSE:\",metrics.mean_squared_error(y_test, y_pred)\n",
    "# 用scikit-learn计算RMSE\n",
    "print \"RMSE:\",np.sqrt(metrics.mean_squared_error(y_test, y_pred))"
   ]
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   "execution_count": null,
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