{"id":3293,"date":"2022-02-27T14:39:12","date_gmt":"2022-02-27T06:39:12","guid":{"rendered":"https:\/\/egonlin.com\/?p=3293"},"modified":"2022-02-27T14:39:12","modified_gmt":"2022-02-27T06:39:12","slug":"%e7%ac%ac%e4%b8%83%e8%8a%82%ef%bc%9a%e7%bb%86%e5%88%86%e6%9e%84%e5%bb%ba%e6%9c%ba%e5%99%a8%e5%ad%a6%e4%b9%a0%e5%ba%94%e7%94%a8%e7%a8%8b%e5%ba%8f%e7%9a%84%e6%b5%81%e7%a8%8b-%e8%ae%ad%e7%bb%83%e6%a8%a1","status":"publish","type":"post","link":"https:\/\/egonlin.com\/?p=3293","title":{"rendered":"\u7b2c\u4e03\u8282\uff1a\u7ec6\u5206\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u7684\u6d41\u7a0b-\u8bad\u7ec3\u6a21\u578b"},"content":{"rendered":"

\u7ec6\u5206\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u7684\u6d41\u7a0b-\u8bad\u7ec3\u6a21\u578b<\/h1>\n

\"\"<\/div><\/p>\n

1.1 \u8bad\u7ec3\u56de\u5f52\u6a21\u578b<\/h1>\n

  \u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u7528\u6ce2\u58eb\u987f\u623f\u4ef7\u6570\u636e\u96c6\u6765\u4ecb\u7ecd\u6211\u4eec\u7684\u56de\u5f52\u6a21\u578b\uff0c\u6ce2\u58eb\u987f\u603b\u5171\u6709506\u6761\u6570\u636e\uff0c\u6240\u4ee5\u6837\u672c\u6570\u5c0f\u4e8e100K\uff0c\u4f9d\u636e\u5730\u56fe\u53ef\u4ee5\u5148\u4f7f\u7528Lasso\u56de\u5f52-\u5f39\u6027\u7f51\u7edc\u56de\u5f52-\u5cad\u56de\u5f52-\u7ebf\u6027\u652f\u6301\u5411\u91cf\u56de\u5f52-\u6838\u652f\u6301\u5411\u91cf\u56de\u5f52-\u51b3\u7b56\u6811\u56de\u5f52-\u968f\u673a\u68ee\u6797\u56de\u5f52<\/p>\n

import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties\nfrom sklearn import datasets\n%matplotlib inline\nfont = FontProperties(fname='\/Library\/Fonts\/Heiti.ttc')\n# \u8bbe\u7f6enumpy\u6570\u7ec4\u7684\u5143\u7d20\u7684\u7cbe\u5ea6\uff08\u4f4d\u6570\uff09\nnp.set_printoptions(precision=4, suppress=True)<\/code><\/pre>\n
boston = datasets.load_boston()\nlen(boston.target)<\/code><\/pre>\n
506<\/code><\/pre>\n
X = boston.data\nX[:5]<\/code><\/pre>\n
array([[  0.0063,  18.    ,   2.31  ,   0.    ,   0.538 ,   6.575 ,\n         65.2   ,   4.09  ,   1.    , 296.    ,  15.3   , 396.9   ,\n          4.98  ],\n       [  0.0273,   0.    ,   7.07  ,   0.    ,   0.469 ,   6.421 ,\n         78.9   ,   4.9671,   2.    , 242.    ,  17.8   , 396.9   ,\n          9.14  ],\n       [  0.0273,   0.    ,   7.07  ,   0.    ,   0.469 ,   7.185 ,\n         61.1   ,   4.9671,   2.    , 242.    ,  17.8   , 392.83  ,\n          4.03  ],\n       [  0.0324,   0.    ,   2.18  ,   0.    ,   0.458 ,   6.998 ,\n         45.8   ,   6.0622,   3.    , 222.    ,  18.7   , 394.63  ,\n          2.94  ],\n       [  0.0691,   0.    ,   2.18  ,   0.    ,   0.458 ,   7.147 ,\n         54.2   ,   6.0622,   3.    , 222.    ,  18.7   , 396.9   ,\n          5.33  ]])<\/code><\/pre>\n
y = boston.target\ny<\/code><\/pre>\n
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])<\/code><\/pre>\n
from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.3, random_state=1, shuffle=True)\nprint('\u8bad\u7ec3\u96c6\u957f\u5ea6:{}'.format(len(y_train)), '\u6d4b\u8bd5\u96c6\u957f\u5ea6:{}'.format(len(y_test)))\n\nscaler = MinMaxScaler()\nscaler = scaler.fit(X_train)\nX_train, X_test = scaler.transform(X_train), scaler.transform(X_test)\nprint('\u6807\u51c6\u5316\u540e\u7684\u8bad\u7ec3\u6570\u636e:\\n{}'.format(X_train[:5]))\nprint('\u6807\u51c6\u5316\u540e\u7684\u6d4b\u8bd5\u6570\u636e:\\n{}'.format(X_test[:5]))<\/code><\/pre>\n
\u8bad\u7ec3\u96c6\u957f\u5ea6:354 \u6d4b\u8bd5\u96c6\u957f\u5ea6:152\n\u6807\u51c6\u5316\u540e\u7684\u8bad\u7ec3\u6570\u636e:\n[[0.0085 0.     0.2815 0.     0.3148 0.4576 0.5936 0.3254 0.1304 0.229\n  0.8936 1.     0.1802]\n [0.0022 0.25   0.1712 0.     0.1399 0.4608 0.9298 0.5173 0.3043 0.1851\n  0.7553 0.9525 0.3507]\n [0.1335 0.     0.6466 0.     0.5885 0.6195 0.9872 0.0208 1.     0.9141\n  0.8085 1.     0.5384]\n [0.0003 0.75   0.0913 0.     0.0885 0.5813 0.1681 0.3884 0.087  0.124\n  0.6064 0.9968 0.0715]\n [0.0619 0.     0.6466 0.     0.6852 0.     0.8713 0.044  1.     0.9141\n  0.8085 0.8936 0.1487]]\n\u6807\u51c6\u5316\u540e\u7684\u6d4b\u8bd5\u6570\u636e:\n[[0.0006 0.33   0.063  0.     0.179  0.63   0.684  0.1867 0.2609 0.0668\n  0.617  1.     0.16  ]\n [0.0003 0.55   0.1217 0.     0.2037 0.6007 0.5362 0.4185 0.1739 0.3492\n  0.5319 1.     0.1504]\n [0.003  0.     0.2364 0.     0.1296 0.4731 0.8457 0.4146 0.087  0.0878\n  0.5638 0.9895 0.471 ]\n [0.0007 0.125  0.2056 0.     0.0494 0.444  0.1638 0.4882 0.1304 0.3015\n  0.6702 0.9983 0.1758]\n [0.0499 0.     0.6466 0.     0.7922 0.3451 0.9596 0.0886 1.     0.9141\n  0.8085 0.9594 0.2334]]<\/code><\/pre>\n
1.1.1 Lasso\u56de\u5f52<\/h2>\n
from sklearn.linear_model import Lasso\n\n# Lasso\u56de\u5f52\u76f8\u5f53\u4e8e\u5728\u666e\u901a\u7ebf\u6027\u56de\u5f52\u4e2d\u52a0\u4e0a\u4e86L1\u6b63\u5219\u5316\u9879\nreg = Lasso()\nreg = reg.fit(X_train, y_train)\ny_pred = reg.predict(X_test)\nprint('\u6240\u6709\u6837\u672c\u8bef\u5dee\u7387:\\n{}'.format(np.abs(y_pred\/y_test-1)))\n'Lasso\u56de\u5f52R2\u5206\u6570:{}'.format(reg.score(X_test, y_test))<\/code><\/pre>\n
\u6240\u6709\u6837\u672c\u8bef\u5dee\u7387:\n[0.1634 0.0095 0.2647 0.0664 0.1048 0.0102 0.131  0.5394 0.0141 0.0309\n 0.0066 0.2222 0.1498 0.1839 0.1663 0.1924 0.4366 0.5148 0.0201 0.2684\n 0.3998 0.3775 0.0257 0.0433 0.032  1.374  0.5287 0.3086 0.4512 0.7844\n 0.0015 0.139  0.5067 0.7093 0.1734 0.0941 0.4246 0.3343 0.6761 0.1453\n 0.0459 0.1484 0.2167 0.4865 0.4501 1.391  0.5023 0.6975 0.0773 0.23\n 0.2403 0.0244 0.1593 0.0433 1.2819 0.071  1.9233 0.0837 0.254  0.4944\n 0.3093 0.1044 1.4394 0.4663 0.2188 0.3503 0.4062 0.4142 0.0567 0.0024\n 0.0396 0.7911 0.0091 0.0746 0.0823 0.0517 0.5071 0.0651 0.0139 0.3429\n 0.21   0.1955 0.3098 0.4897 0.0459 0.0748 0.6058 0.018  0.2064 0.1833\n 0.0054 0.517  0.556  0.0191 1.0166 0.1782 0.0312 0.0239 0.4559 0.0291\n 1.1285 0.3624 0.0518 0.0192 1.531  0.0605 0.8266 0.1089 0.2467 0.1109\n 0.4345 0.0151 0.8514 0.2863 0.3463 0.3223 0.2149 0.1205 0.2873 0.5277\n 0.1933 0.4103 0.0897 0.1084 0.0671 0.0542 0.023  0.1279 0.0502 0.139\n 0.1033 0.0069 0.0441 1.0007 0.0099 0.3426 0.4286 0.6492 0.4074 1.0538\n 0.1672 0.1838 0.0782 0.0069 0.1382 0.0446 0.0055 0.0687 0.1621 0.0338\n 0.316  0.4306]\n\n'Lasso\u56de\u5f52R2\u5206\u6570:0.21189040113362279'<\/code><\/pre>\n
1.1.2 \u5f39\u6027\u7f51\u7edc\u56de\u5f52<\/h2>\n
from sklearn.linear_model import ElasticNet\n\n# \u5f39\u6027\u7f51\u7edc\u56de\u5f52\u76f8\u5f53\u4e8e\u5728\u666e\u901a\u7ebf\u6027\u56de\u5f52\u4e2d\u52a0\u4e0a\u4e86\u52a0\u6743\u7684\uff08L1\u6b63\u5219\u5316\u9879+L2\u6b63\u5219\u5316\u9879\uff09\nreg = ElasticNet()\nreg = reg.fit(X_train, y_train)\ny_pred = reg.predict(X_test)\n'\u5f39\u6027\u7f51\u7edc\u56de\u5f52R2\u5206\u6570:{}'.format(reg.score(X_test, y_test))<\/code><\/pre>\n
'\u5f39\u6027\u7f51\u7edc\u56de\u5f52R2\u5206\u6570:0.1414319491120538'<\/code><\/pre>\n
1.1.3 \u5cad\u56de\u5f52<\/h2>\n
from sklearn.linear_model import Ridge\n\n# \u5cad\u56de\u5f52\u76f8\u5f53\u4e8e\u5728\u666e\u901a\u7ebf\u6027\u56de\u5f52\u4e2d\u52a0\u4e0a\u4e86L2\u6b63\u5219\u5316\u9879\nreg = Ridge()\nreg = reg.fit(X_train, y_train)\ny_pred = reg.predict(X_test)\n'\u5cad\u56de\u5f52R2\u5206\u6570:{}'.format(reg.score(X_test, y_test))<\/code><\/pre>\n
'\u5cad\u56de\u5f52R2\u5206\u6570:0.7718570925003422'<\/code><\/pre>\n
1.1.4 \u7ebf\u6027\u652f\u6301\u5411\u91cf\u56de\u5f52<\/h2>\n
from sklearn.svm import LinearSVR\n\n# \u7ebf\u6027\u652f\u6301\u5411\u91cf\u56de\u5f52\u4f7f\u7528\u7684\u662f\u786c\u95f4\u9694\u6700\u5927\u5316\uff0c\u53ef\u4ee5\u5904\u7406\u5f02\u5e38\u503c\u5bfc\u81f4\u7684\u6570\u636e\u7ebf\u6027\u4e0d\u53ef\u5206\nreg = LinearSVR(C=100, max_iter=10000)\nreg = reg.fit(X_train, y_train)\ny_pred = reg.predict(X_test)\n'\u7ebf\u6027\u652f\u6301\u5411\u91cf\u56de\u5f52R2\u5206\u6570:{}'.format(reg.score(X_test, y_test))<\/code><\/pre>\n
'\u7ebf\u6027\u652f\u6301\u5411\u91cf\u56de\u5f52R2\u5206\u6570:0.7825143888611817'<\/code><\/pre>\n
1.1.5 \u6838\u652f\u6301\u5411\u91cf\u56de\u5f52<\/h2>\n
<\/p>
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\u67e5\u770b\u66f4\u591a<\/h2>
\u8054\u7cfb\u7ba1\u7406\u5458\u5fae\u4fe1tutu19192010\uff0c\u6ce8\u518c\u8d26\u53f7<\/p>\n<\/div>
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\u7ec6\u5206\u6784\u5efa\u673a\u5668\u5b66\u4e60\u5e94\u7528\u7a0b\u5e8f\u7684\u6d41\u7a0b-\u8bad\u7ec3\u6a21\u578b 1.1 \u8bad\u7ec3\u56de\u5f52\u6a21\u578b   \u63a5\u4e0b\u6765\u6211\u4eec\u5c06\u7528\u6ce2\u58eb\u987f\u623f […]<\/p>\n","protected":false},"author":3,"featured_media":3275,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[276,301],"tags":[],"_links":{"self":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3293"}],"collection":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3293"}],"version-history":[{"count":0,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/posts\/3293\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=\/wp\/v2\/media\/3275"}],"wp:attachment":[{"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3293"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3293"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/egonlin.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3293"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}