Lesson16-实战Ridge & Lasso Regression解决线性回归的Overfitting过度拟合.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# 第十六课 实战Ridge & Lasso Regression解决线性回归的Overfitting过度拟合\n",
    "\n",
    "\n",
    "Python零基础学习：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "源文件下载链接: https://pan.baidu.com/s/1yuNlG6u9_C31fzhzbzqASA 提取码: ebrh\n",
    "\n",
    "Random state ensures that the splits that you generate are reproducible. Scikit-learn uses random permutations to generate the splits. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.\n",
    "\n",
    "If you don't specify the random_state in the code, then every time you run(execute) your code a new random value is generated and the train and test datasets would have different values each time.\n",
    "\n",
    "However, if a fixed value is assigned like random_state = 0 or 1 or 42 or any other integer then no matter how many times you execute your code the result would be the same .i.e, same values in train and test datasets.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston=load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
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       "        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
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       "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n",
       " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
       "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
       " 'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
       " 'filename': 'C:\\\\Users\\\\raymond\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\boston_house_prices.csv'}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=pd.DataFrame(boston['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
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       "0    396.90  4.98  \n",
       "1    396.90  9.14  \n",
       "2    392.83  4.03  \n",
       "3    394.63  2.94  \n",
       "4    396.90  5.33  \n",
       "..      ...   ...  \n",
       "501  391.99  9.67  \n",
       "502  396.90  9.08  \n",
       "503  396.90  5.64  \n",
       "504  393.45  6.48  \n",
       "505  396.90  7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.columns=boston['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "    <tr>\n",
       "      <th>505</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression 要点一：为什么没使用14课中的train_test_split及训练集测试集？\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "linregressor=LinearRegression()\n",
    "cvscore=cross_val_score(linregressor,X,y,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.63919994,  0.71386698,  0.58702344,  0.07923081, -0.25294154])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_cvscore=np.mean(cvscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.353275924395884\n"
     ]
    }
   ],
   "source": [
    "print(mean_cvscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression 要点二：加入parameters多个备选参数由算法决定最佳调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
       "                             max_iter=None, normalize=False, random_state=None,\n",
       "                             solver='auto', tol=0.001),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'alpha': [1e-15, 1e-10, 1e-08, 0.001, 0.01, 1, 5, 10,\n",
       "                                   20, 30, 35, 40, 45, 50, 55, 100, 200, 500,\n",
       "                                   1000]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "ridge=Ridge()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100,200,500,1000]}\n",
    "ridgeregressor=GridSearchCV(ridge,parameters,cv=5)\n",
    "ridgeregressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 200}\n",
      "0.49798762179623146\n"
     ]
    }
   ],
   "source": [
    "print(ridgeregressor.best_params_)\n",
    "print(ridgeregressor.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ridge Regression 要点三：算法决定最佳调参后可以再进一步精细化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=Ridge(alpha=1.0, copy_X=True, fit_intercept=True,\n",
       "                             max_iter=None, normalize=False, random_state=None,\n",
       "                             solver='auto', tol=0.001),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'alpha': [160, 170, 180, 190, 200, 210, 230]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ridge=Ridge()\n",
    "parameters={'alpha':[160,170,180,190,200,210,230]}\n",
    "ridgeregressor=GridSearchCV(ridge,parameters,cv=5)\n",
    "ridgeregressor.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 180}\n",
      "0.4982367681072657\n"
     ]
    }
   ],
   "source": [
    "print(ridgeregressor.best_params_)\n",
    "print(ridgeregressor.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4685.292042663493, tolerance: 3.919148542079209\n",
      "  positive)\n",
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4175.730901660939, tolerance: 3.3071316790123473\n",
      "  positive)\n",
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4102.124474784773, tolerance: 2.813643886419753\n",
      "  positive)\n",
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 2554.8592235736, tolerance: 3.3071762123456785\n",
      "  positive)\n",
      "C:\\Users\\raymond\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 4441.71793002723, tolerance: 3.480910444444445\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1}\n",
      "0.4318487879265221\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "lasso=Lasso()\n",
    "parameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40,45,50,55,100,200,300,500]}\n",
    "lassoregressor=GridSearchCV(lasso,parameters,cv=5)\n",
    "\n",
    "lassoregressor.fit(X,y)\n",
    "print(lassoregressor.best_params_)\n",
    "print(lassoregressor.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1, copy_X=True, fit_intercept=False, max_iter=10000000000000,\n",
       "      normalize=False, positive=True, precompute=False, random_state=None,\n",
       "      selection='cyclic', tol=1e-14, warm_start=False)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lasso(alpha=1, fit_intercept=False, tol=0.00000000000001,max_iter=10000000000000, positive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_lasso=lassoregressor.predict(X_test)\n",
    "prediction_ridge=ridgeregressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.8221835 , 25.20259785, 27.53093947, 14.58951793, 19.6074286 ,\n",
       "       21.69483525, 21.4566671 , 23.61199647, 20.7368666 , 19.58217475,\n",
       "        6.63735854, 13.94947909, 18.49317999,  7.72499122, 36.3522899 ,\n",
       "       31.18961686, 22.33460779, 34.97976822, 30.83078488, 24.46734842,\n",
       "       25.47472001, 24.6587079 , 20.17171431, 30.01063275, 22.97537586,\n",
       "       15.45690209, 18.90690169, 21.75900952, 32.56455397, 19.74393167,\n",
       "       18.12048305, 20.06946532, 20.97263715, 23.81262096, 28.96841294,\n",
       "       20.7965572 , 10.95091512, 25.35142051, 16.23333426, 13.4982668 ,\n",
       "       24.98278871, 20.39690622, 21.61521524, 16.39149259, 22.65891092,\n",
       "       25.1457976 , 18.73402526, 23.8338197 , 12.33037924, 23.8180197 ,\n",
       "       21.52664928, 17.3346035 , 24.96628962, 27.89463941, 13.76088933,\n",
       "       21.3597084 , 20.75545937, 15.80907985, 16.20859966, 19.5075779 ,\n",
       "       19.53138931, 21.89707787, 31.58387677, 31.47599653, 18.42654404,\n",
       "       31.76658571, 19.22691897, 18.3864344 , 16.63161167, 22.86516713,\n",
       "       21.02865109, 22.53088304, 30.94096598, 29.29650859, 25.49106868,\n",
       "        5.78908343, 34.4337712 , 23.33656159, 26.96978297, 20.25620449,\n",
       "       28.11741719, 18.67996191, 21.59515998, 35.26835166, 35.72684936,\n",
       "       24.86758266, 24.10298707, 15.63298048, 29.91089724, 17.73858371,\n",
       "       15.66579636, 12.72751693, 25.67752193, 30.42846915, 24.23451387,\n",
       "       21.21831042,  0.98960907, 26.86571219, 15.86202673, 19.76164988,\n",
       "       24.74384217, 21.88867704, 29.62234186, 21.06186208, 25.83065884,\n",
       "       24.01971497,  7.16306546, 19.25558114, 23.75587463, 27.99982286,\n",
       "       32.54691099, 11.73805049, 19.42074886, 19.94098903, 10.70893573,\n",
       "       20.76435055,  3.62607357, 20.34636874, 11.17173902, 40.16519422,\n",
       "       30.02425912, 13.45395973, 17.79873118, 21.96147555, 24.69986329,\n",
       "       20.247967  , 34.27793925, 14.21222226, 23.02723386, 31.97584528,\n",
       "       20.01170255, 10.77860602, 11.51069203, 22.51737822, 13.03965308,\n",
       "       31.63966819, 25.66605299, 15.34351723, 24.27415491,  7.25512512,\n",
       "       14.13475988, 21.17380907, 32.50474818, 28.27117135, 24.08805641,\n",
       "       17.11766404, 30.12230361, 27.88432928, 13.52652152,  7.85791541,\n",
       "       29.48022091, 26.71256632])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([23.74549008, 25.05669894, 27.30699681, 15.26474466, 20.28388223,\n",
       "       22.75172373, 21.45598059, 24.23856649, 21.11819135, 19.95909495,\n",
       "        8.98313974, 13.30218316, 17.99925624,  8.39272295, 35.35448484,\n",
       "       30.78862119, 22.38396018, 34.3159854 , 29.8521009 , 24.63915134,\n",
       "       25.7851669 , 25.20997813, 20.07312698, 29.34704456, 23.54801802,\n",
       "       17.49320154, 19.76581421, 22.02254005, 31.35043449, 19.53061141,\n",
       "       17.86731915, 20.03959005, 22.70589322, 24.27507125, 28.3321545 ,\n",
       "       20.26286175, 10.68729818, 24.94704305, 15.84336797, 12.8254677 ,\n",
       "       25.41186454, 20.63647768, 22.05355817, 16.77749717, 23.30629191,\n",
       "       25.77681736, 19.84379805, 23.32853181, 12.22570134, 23.7054699 ,\n",
       "       21.09270778, 16.76593695, 24.7895207 , 27.77843148, 15.02615536,\n",
       "       21.29343807, 21.55732101, 17.59916216, 18.35971801, 19.53427781,\n",
       "       21.69584327, 22.09466825, 30.77701755, 30.58126341, 18.59617266,\n",
       "       31.00696923, 18.55257582, 18.71479862, 15.79040727, 22.86722623,\n",
       "       21.5804364 , 23.05453916, 30.92223126, 29.21101697, 25.40070956,\n",
       "        5.75970797, 32.88156033, 23.539222  , 26.54430218, 20.38280007,\n",
       "       28.11924474, 19.21283372, 21.96930246, 33.47811935, 33.60083428,\n",
       "       25.39263122, 23.9380826 , 15.60389015, 30.00273357, 17.19181248,\n",
       "       16.84840301, 12.0032218 , 27.14671556, 30.55661543, 25.00026849,\n",
       "       22.52772776,  1.12415573, 28.040647  , 15.35965294, 21.08232769,\n",
       "       24.57837072, 21.89156861, 28.27698699, 21.66366909, 25.55908614,\n",
       "       24.45293184,  7.59070061, 19.83235028, 24.23228257, 27.37347087,\n",
       "       32.42177577, 11.10402837, 19.85336661, 19.73688998, 11.03931099,\n",
       "       20.59144514,  2.72833746, 21.36245219, 11.28411586, 37.86559235,\n",
       "       29.89817646, 13.04488675, 18.83924185, 21.70908522, 25.04529112,\n",
       "       20.9043036 , 32.67161117, 15.77775499, 24.06679765, 30.81637705,\n",
       "       19.37042737,  9.64783788, 12.72310891, 22.90689725, 11.8868096 ,\n",
       "       31.55422108, 25.35032222, 14.60847289, 24.91415537,  6.60516972,\n",
       "       13.54959312, 20.65191891, 31.43726612, 28.6229339 , 24.13609407,\n",
       "       18.33302342, 29.84498063, 28.03248999, 12.83495092,  7.52947558,\n",
       "       29.7816876 , 27.51646227])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.histplot(prediction_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2f67f2bed48>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.displot(prediction_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}