{"id":52,"date":"2023-09-18T16:49:23","date_gmt":"2024-03-01T14:55:06","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/index.php\/2023\/11\/30\/%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8scikit-learn%e5%9c%a8python%e4%b8%ad%e5%bd%92%e4%b8%80%e5%8c%96%e6%95%b0%e6%8d%ae\/"},"modified":"2025-07-31T22:06:34","modified_gmt":"2025-07-31T14:06:34","slug":"%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8scikit-learn%e5%9c%a8python%e4%b8%ad%e5%bd%92%e4%b8%80%e5%8c%96%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8scikit-learn%e5%9c%a8python%e4%b8%ad%e5%bd%92%e4%b8%80%e5%8c%96%e6%95%b0%e6%8d%ae\/","title":{"rendered":"Python\u6570\u636e\u5f52\u4e00\u5316\uff1a\u4f7f\u7528Scikit-learn\u63d0\u5347\u673a\u5668\u5b66\u4e60\u6a21\u578b\u6027\u80fd"},"content":{"rendered":"

\u5f15\u8a00<\/h3>\n

\u5728\u672c\u6587\u4e2d\uff0c\u4f60\u5c06\u5c1d\u8bd5\u4f7f\u7528scikit-learn\uff08\u4e5f\u88ab\u79f0\u4e3asklearn\uff09\u5728Python\u4e2d\u8fdb\u884c\u6570\u636e\u5f52\u4e00\u5316\u7684\u4e00\u4e9b\u4e0d\u540c\u65b9\u6cd5\u3002\u5f53\u4f60\u5f52\u4e00\u5316\u6570\u636e\u65f6\uff0c\u4f60\u6539\u53d8\u4e86\u6570\u636e\u7684\u8303\u56f4\u3002\u6570\u636e\u901a\u5e38\u88ab\u91cd\u65b0\u7f29\u653e\u52300\u548c1\u4e4b\u95f4\uff0c\u56e0\u4e3a\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5728\u7279\u5f81\u8f83\u5c0f\u7684\u8303\u56f4\u4e0a\u8868\u73b0\u5f97\u66f4\u597d\u6216\u6536\u655b\u5f97\u66f4\u5feb\u3002\u5728\u5bf9\u6570\u636e\u8fdb\u884c\u673a\u5668\u5b66\u4e60\u6a21\u578b\u8bad\u7ec3\u4e4b\u524d\uff0c\u901a\u5e38\u4f1a\u5148\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\uff0c\u4ee5\u83b7\u5f97\u66f4\u597d\u3001\u66f4\u5feb\u7684\u7ed3\u679c\u3002\u5f52\u4e00\u5316\u8fd8\u4f7f\u5f97\u8bad\u7ec3\u8fc7\u7a0b\u5bf9\u7279\u5f81\u7684\u8303\u56f4\u4e0d\u654f\u611f\uff0c\u8bad\u7ec3\u540e\u7684\u7cfb\u6570\u4e5f\u66f4\u4f18\u3002<\/p>\n

\u901a\u8fc7\u91cd\u65b0\u7f29\u653e\u4ee5\u4f7f\u7279\u5f81\u66f4\u9002\u5408\u8bad\u7ec3\u7684\u8fc7\u7a0b\u79f0\u4e3a\u7279\u5f81\u7f29\u653e<\/strong>\u3002<\/p>\n

\u672c\u6559\u7a0b\u4f7f\u7528Python\u7248\u672c3.9.13\u548cscikit-learn\u7248\u672c1.0.2\u8fdb\u884c\u4e86\u6d4b\u8bd5\u3002<\/p>\n

\u4f7f\u7528scikit-learn\u7684preprocessing.normalize()<\/code>\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u6807\u51c6\u5316<\/h2>\n

\u4f60\u53ef\u4ee5\u4f7f\u7528scikit-learn\u7684preprocessing.normalize()<\/code>\u51fd\u6570\u6765\u5f52\u4e00\u5316\u7c7b\u6570\u7ec4\u6570\u636e\u96c6\u3002<\/p>\n

normalize()<\/code>\u51fd\u6570\u5c06\u5411\u91cf\u5355\u72ec\u7f29\u653e\u5230\u5355\u4f4d\u8303\u6570\uff0c\u4f7f\u5f97\u5411\u91cf\u7684\u957f\u5ea6\u4e3a1\u3002normalize()<\/code>\u51fd\u6570\u7684\u9ed8\u8ba4\u8303\u6570\u662fL2\u8303\u6570\uff0c\u4e5f\u79f0\u4e3a\u6b27\u51e0\u91cc\u5f97\u8303\u6570\u3002L2\u8303\u6570\u7684\u516c\u5f0f\u662f\u6bcf\u4e2a\u503c\u7684\u5e73\u65b9\u548c\u7684\u5e73\u65b9\u6839\u3002\u5c3d\u7ba1\u4f7f\u7528normalize()<\/code>\u51fd\u6570\u4f1a\u4f7f\u503c\u4ecb\u4e8e0\u548c1\u4e4b\u95f4\uff0c\u4f46\u8fd9\u4e0e\u7b80\u5355\u5730\u5c06\u503c\u7f29\u653e\u52300\u548c1\u4e4b\u95f4\u662f\u4e0d\u540c\u7684\u3002<\/p>\n

\u4f7f\u7528normalize()<\/code>\u51fd\u6570\u5f52\u4e00\u5316\u6570\u7ec4<\/h3>\n

\u4f60\u53ef\u4ee5\u4f7f\u7528normalize()<\/code>\u51fd\u6570\u5f52\u4e00\u5316\u4e00\u7ef4NumPy\u6570\u7ec4\u3002<\/p>\n

\u5bfc\u5165sklearn.preprocessing<\/code>\u6a21\u5757\uff1a<\/p>\n

from<\/span> sklearn import<\/span> preprocessing\r\n<\/code><\/pre>\n
\u5bfc\u5165NumPy\u5e76\u521b\u5efa\u4e00\u4e2a\u6570\u7ec4\uff1a<\/p>\n
import<\/span> numpy as<\/span> np\r\n\r\nx_array =<\/span> np.<\/span>array(<\/span>[<\/span>2<\/span>,<\/span>3<\/span>,<\/span>5<\/span>,<\/span>6<\/span>,<\/span>7<\/span>,<\/span>4<\/span>,<\/span>8<\/span>,<\/span>7<\/span>,<\/span>6<\/span>]<\/span>)<\/span>\r\n<\/code><\/pre>\n
\u5bf9\u6570\u7ec4\u4f7f\u7528normalize()<\/code>\u51fd\u6570\uff0c\u6cbf\u884c\u5f52\u4e00\u5316\u6570\u636e\uff0c\u6b64\u5904\u4e3a\u4e00\u7ef4\u6570\u7ec4\uff1a<\/p>\n
normalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n
\u8fd0\u884c\u5b8c\u6574\u7684\u793a\u4f8b\u4ee3\u7801\uff0c\u6f14\u793a\u5982\u4f55\u4f7f\u7528normalize()<\/code>\u51fd\u6570\u5f52\u4e00\u5316NumPy\u6570\u7ec4\uff1a<\/p>\n
norm_numpy.py<\/p>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> numpy as<\/span> np\r\n\r\nx_array =<\/span> np.<\/span>array(<\/span>[<\/span>2<\/span>,<\/span>3<\/span>,<\/span>5<\/span>,<\/span>6<\/span>,<\/span>7<\/span>,<\/span>4<\/span>,<\/span>8<\/span>,<\/span>7<\/span>,<\/span>6<\/span>]<\/span>)<\/span>\r\n\r\nnormalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n<\/div>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa<\/p>\n
[[0.11785113 0.1767767 0.29462783 0.35355339 0.41247896 0.23570226 0.47140452 0.41247896 0.35355339]]\r\n<\/code><\/pre>\n<\/div>\n
\u8f93\u51fa\u7ed3\u679c\u663e\u793a\u6240\u6709\u503c\u90fd\u57280\u52301\u7684\u8303\u56f4\u5185\u3002\u5982\u679c\u4f60\u5c06\u8f93\u51fa\u4e2d\u7684\u6bcf\u4e2a\u503c\u5e73\u65b9\u7136\u540e\u76f8\u52a0\uff0c\u7ed3\u679c\u5c06\u662f1\uff0c\u6216\u8005\u975e\u5e38\u63a5\u8fd11\u3002<\/p>\n
\u4f7f\u7528normalize()<\/code>\u51fd\u6570\u5f52\u4e00\u5316DataFrame\u7684\u5217<\/h3>\n
\u8fd9\u662f\u6587\u7ae0\u300a\u5982\u4f55\u4f7f\u7528scikit-learn\u5728Python\u4e2d\u5f52\u4e00\u5316\u6570\u636e\u300b\u7684\u7b2c2\u90e8\u5206\uff08\u51716\u90e8\u5206\uff09\u3002<\/p>\n
\u5728pandas DataFrame\u4e2d\uff0c\u7279\u5f81\u662f\u5217\uff0c\u6837\u672c\u662f\u884c\u3002\u60a8\u53ef\u4ee5\u5c06DataFrame\u5217\u8f6c\u6362\u4e3aNumPy\u6570\u7ec4\uff0c\u7136\u540e\u5bf9\u6570\u7ec4\u4e2d\u7684\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5904\u7406\u3002<\/p>\n
\u672c\u8282\u53ca\u540e\u7eed\u7ae0\u8282\u7684\u793a\u4f8b\u5747\u4f7f\u7528\u52a0\u5229\u798f\u5c3c\u4e9a\u4f4f\u623f\u6570\u636e\u96c6\uff08California Housing dataset\uff09\u3002<\/p>\n
\u793a\u4f8b\u4ee3\u7801\u7684\u7b2c\u4e00\u90e8\u5206\u5bfc\u5165\u6a21\u5757\u3001\u52a0\u8f7d\u6570\u636e\u96c6\u3001\u521b\u5efaDataFrame\u5e76\u6253\u5370\u6570\u636e\u96c6\u63cf\u8ff0\uff1a<\/p>\n
import<\/span> numpy as<\/span> np\r\nfrom<\/span> sklearn import<\/span> preprocessing\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\n\r\n# \u521b\u5efaDataFrame<\/span>\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\n# \u6253\u5370\u6570\u636e\u96c6\u63cf\u8ff0<\/span>\r\nprint<\/span>(<\/span>california_housing.<\/span>DESCR)<\/span>\r\n<\/code><\/pre>\n
\u8bf7\u6ce8\u610f\uff0cas_frame<\/code> \u53c2\u6570\u8bbe\u7f6e\u4e3a True<\/code> \u4ee5\u5c06 california_housing<\/code> \u5bf9\u8c61\u521b\u5efa\u4e3a pandas DataFrame\u3002<\/p>\n
\u8f93\u51fa\u5305\u542b\u6570\u636e\u96c6\u63cf\u8ff0\u7684\u4ee5\u4e0b\u6458\u5f55\uff0c\u60a8\u53ef\u4ee5\u6839\u636e\u6b64\u6458\u5f55\u9009\u62e9\u8981\u5f52\u4e00\u5316\u7684\u7279\u5f81\uff1a<\/p>\n
\u8f93\u51fa<\/div>\n
.. _california_housing_dataset:\r\n\u52a0\u5229\u798f\u5c3c\u4e9a\u4f4f\u623f\u6570\u636e\u96c6\r\n--------------------------\r\n**\u6570\u636e\u96c6\u7279\u5f81:**\r\n:\u5b9e\u4f8b\u6570\u91cf: 20640\r\n:\u5c5e\u6027\u6570\u91cf: 8\u4e2a\u6570\u503c\u578b\u9884\u6d4b\u5c5e\u6027\u548c\u76ee\u6807\u5c5e\u6027\r\n:\u5c5e\u6027\u4fe1\u606f:\r\n  - MedInc \u8857\u533a\u7ec4\u7684\u6536\u5165\u4e2d\u4f4d\u6570\r\n  - HouseAge \u8857\u533a\u7ec4\u7684\u623f\u5c4b\u5e74\u9f84\u4e2d\u4f4d\u6570\r\n  - AveRooms \u6bcf\u6237\u5e73\u5747\u623f\u95f4\u6570\r\n  - AveBedrms \u6bcf\u6237\u5e73\u5747\u5367\u5ba4\u6570\r\n  - Population \u8857\u533a\u7ec4\u4eba\u53e3\r\n  - AveOccup \u6bcf\u6237\u5e73\u5747\u6210\u5458\u6570\r\n  - Latitude \u8857\u533a\u7ec4\u7eac\u5ea6\r\n  - Longitude \u8857\u533a\u7ec4\u7ecf\u5ea6\r\n...<\/code><\/pre>\n
\u63a5\u4e0b\u6765\uff0c\u5c06\u4e00\u5217\uff08\u7279\u5f81\uff09\u8f6c\u6362\u4e3a\u6570\u7ec4\u5e76\u6253\u5370\u3002\u672c\u4f8b\u4f7f\u7528\u201cHouseAge\u201d\u5217\uff1a<\/p>\n
x_array =<\/span> np.<\/span>array(<\/span>california_housing[<\/span>'HouseAge'<\/span>]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"HouseAge \u6570\u7ec4: \"<\/span>,<\/span>x_array)<\/span>\r\n<\/code><\/pre>\n
\u6700\u540e\uff0c\u4f7f\u7528 normalize()<\/code> \u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u5f52\u4e00\u5316\u5e76\u6253\u5370\u7ed3\u679c\u6570\u7ec4\uff1a<\/p>\n
normalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"\u5f52\u4e00\u5316\u540e\u7684HouseAge\u6570\u7ec4: \"<\/span>,<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n
\u8fd0\u884c\u5b8c\u6574\u793a\u4f8b\u4ee5\u6f14\u793a\u5982\u4f55\u4f7f\u7528 normalize()<\/code> \u51fd\u6570\u5f52\u4e00\u5316\u7279\u5f81\uff1a<\/p>\n
norm_feature.py<\/div>\n
\u8fd9\u662f\u6587\u7ae0\u300a\u5982\u4f55\u4f7f\u7528scikit-learn\u5728Python\u4e2d\u5f52\u4e00\u5316\u6570\u636e\u300b\u7684\u7b2c3\u90e8\u5206\uff08\u51716\u90e8\u5206\uff09\u3002<\/p>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> numpy as<\/span> np\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\n\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n# print(california_housing.DESCR)<\/span>\r\n\r\nx_array =<\/span> np.<\/span>array(<\/span>california_housing.<\/span>data[<\/span>'HouseAge'<\/span>]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"\u623f\u5c4b\u5e74\u9f84\u6570\u7ec4: \"<\/span>,<\/span>x_array)<\/span>\r\n\r\nnormalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"\u5f52\u4e00\u5316\u540e\u7684\u623f\u5c4b\u5e74\u9f84\u6570\u7ec4: \"<\/span>,<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c<\/p>\n
\u623f\u5c4b\u5e74\u9f84\u6570\u7ec4: [41. 21. 52. ... 17. 18. 16.]\r\n\u5f52\u4e00\u5316\u540e\u7684\u623f\u5c4b\u5e74\u9f84\u6570\u7ec4: [[0.00912272 0.00467261 0.01157028 ... 0.00378259 0.0040051 0.00356009]]\r\n<\/code><\/pre>\n<\/div>\n
\u8f93\u51fa\u7ed3\u679c\u663e\u793a\uff0cnormalize()<\/code> \u51fd\u6570\u6539\u53d8\u4e86\u623f\u5c4b\u4e2d\u4f4d\u5e74\u9f84\u503c\u7684\u6570\u7ec4\uff0c\u4f7f\u5f97\u8fd9\u4e9b\u503c\u7684\u5e73\u65b9\u548c\u7684\u5e73\u65b9\u6839\u7b49\u4e8e\u4e00\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u8fd9\u4e9b\u503c\u4f7f\u7528 L2 \u8303\u6570\u88ab\u7f29\u653e\u5230\u4e86\u5355\u4f4d\u957f\u5ea6\u3002<\/p>\n
\u4f7f\u7528 normalize()<\/code> \u51fd\u6570\u6309\u884c\u6216\u6309\u5217\u5f52\u4e00\u5316\u6570\u636e\u96c6<\/h3>\n
\u5f53\u60a8\u5728\u4e0d\u5c06\u7279\u5f81\u6216\u5217\u8f6c\u6362\u4e3a\u6570\u7ec4\u8fdb\u884c\u5904\u7406\u7684\u60c5\u51b5\u4e0b\u5f52\u4e00\u5316\u6570\u636e\u96c6\u65f6\uff0c\u6570\u636e\u4f1a\u6309\u884c\u8fdb\u884c\u5f52\u4e00\u5316\u3002normalize()<\/code> \u51fd\u6570\u7684\u9ed8\u8ba4\u8f74\u662f 1\uff0c\u8fd9\u610f\u5473\u7740\u6bcf\u4e2a\u6837\u672c\uff08\u5373\u6bcf\u4e00\u884c\uff09\u90fd\u88ab\u5f52\u4e00\u5316\u3002<\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u4e86\u4f7f\u7528\u9ed8\u8ba4\u8f74\u5f52\u4e00\u5316\u52a0\u5dde\u4f4f\u623f\u6570\u636e\u96c6\uff1a<\/p>\n
norm_dataset_sample.py<\/p>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> pandas as<\/span> pd\r\n\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\nd =<\/span> preprocessing.<\/span>normalize(<\/span>california_housing.<\/span>data)<\/span>\r\nscaled_df =<\/span> pd.<\/span>DataFrame(<\/span>d,<\/span> columns=<\/span>california_housing.<\/span>data.<\/span>columns)<\/span>\r\nprint<\/span>(<\/span>scaled_df)<\/span>\r\n<\/code><\/pre>\n<\/div>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c<\/p>\n
<\/code><\/pre>\n<\/div>\n
\u8fd9\u662f\u6587\u7ae0\u300a\u5982\u4f55\u4f7f\u7528scikit-learn\u5728Python\u4e2d\u5f52\u4e00\u5316\u6570\u636e\u300b\u7684\u7b2c4\u90e8\u5206\uff08\u51716\u90e8\u5206\uff09\u3002<\/p>\n
MedInc HouseAge AveRooms ... AveOccup Latitude Longitude\r\n0 0.023848 0.117447 0.020007 ... 0.007321 0.108510 -0.350136\r\n1 0.003452 0.008734 0.002594 ... 0.000877 0.015745 -0.050829\r\n2 0.014092 0.100971 0.016093 ... 0.005441 0.073495 -0.237359\r\n3 0.009816 0.090449 0.010119 ... 0.004432 0.065837 -0.212643\r\n4 0.006612 0.089394 0.010799 ... 0.003750 0.065069 -0.210162\r\n... ... ... ... ... ... ... ...\r\n20635 0.001825 0.029242 0.005902 ... 0.002995 0.046179 -0.141637\r\n20636 0.006753 0.047539 0.016147 ... 0.008247 0.104295 -0.320121\r\n20637 0.001675 0.016746 0.005128 ... 0.002291 0.038840 -0.119405\r\n20638 0.002483 0.023932 0.007086 ... 0.002823 0.052424 -0.161300\r\n20639 0.001715 0.011486 0.003772 ... 0.001879 0.028264 -0.087038\r\n[20640 rows x 8 columns]<\/code><\/pre>\n
\u8f93\u51fa\u663e\u793a\uff0c\u503c\u662f\u6309\u884c\u5f52\u4e00\u5316\u7684\uff0c\u8fd9\u610f\u5473\u7740\u6bcf\u4e2a\u6837\u672c\u90fd\u88ab\u5f52\u4e00\u5316\uff0c\u800c\u4e0d\u662f\u6bcf\u4e2a\u7279\u5f81\u3002<\/p>\n
\u7136\u800c\uff0c\u60a8\u53ef\u4ee5\u901a\u8fc7\u6307\u5b9a\u8f74\uff08axis\uff09\u6765\u6309\u7279\u5f81\u8fdb\u884c\u5f52\u4e00\u5316\u3002<\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u4e86\u5982\u4f55\u4f7f\u7528 axis=0<\/code> \u6309\u7279\u5f81\u5f52\u4e00\u5316\u52a0\u5229\u798f\u5c3c\u4e9a\u4f4f\u623f\u6570\u636e\u96c6\uff1a<\/p>\n
norm_dataset_feature.py<\/p>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> pandas as<\/span> pd\r\n\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\nd =<\/span> preprocessing.<\/span>normalize(<\/span>california_housing.<\/span>data,<\/span> axis=<\/span>0<\/span><\/mark>)<\/span>\r\nscaled_df =<\/span> pd.<\/span>DataFrame(<\/span>d,<\/span> columns=<\/span>california_housing.<\/span>data.<\/span>columns)<\/span>\r\nprint<\/span>(<\/span>scaled_df)<\/span>\r\n<\/code><\/pre>\n<\/div>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa<\/p>\n
MedInc HouseAge AveRooms ... AveOccup Latitude Longitude\r\n0 0.013440 0.009123 0.008148 ... 0.001642 0.007386 -0.007114\r\n1 0.013401 0.004673 0.007278 ... 0.001356 0.007383 -0.007114\r\n2 0.011716 0.011570 0.009670 ... 0.001801 0.007381 -0.007115\r\n3 0.009110 0.011570 0.006787 ... 0.001638 0.007381 -0.007116\r\n4 0.006209 0.011570 0.007329 ... 0.001402 0.007381 -0.007116\r\n... ... ... ... ... ... ... ...\r\n20635 0.002519 0.005563 0.005886 ... 0.001646 0.007698 -0.007048\r\n20636 0.004128 0.004005 0.007133 ... 0.002007 0.007700 -0.007055\r\n20637 0.002744 0.003783 0.006073 ... 0.001495 0.007689 -0.007056\r\n20638 0.003014 0.004005 0.006218 ... 0.001365 0.007689 -0.007061\r\n20639 0.003856 0.003560 0.006131 ... 0.001682 0.007677 -0.007057\r\n[20640 rows x 8 columns]<\/code><\/pre>\n<\/div>\n
\u5f53\u60a8\u68c0\u67e5\u8f93\u51fa\u65f6\uff0c\u4f1a\u6ce8\u610f\u5230\u201cHouseAge\u201d\u5217\u7684\u7ed3\u679c\u4e0e\u60a8\u5728\u524d\u9762\u7684\u793a\u4f8b\u4e2d\u5c06\u201cHouseAge\u201d\u5217\u8f6c\u6362\u4e3a\u6570\u7ec4\u5e76\u8fdb\u884c\u5f52\u4e00\u5316\u65f6\u5f97\u5230\u7684\u7ed3\u679c\u4e00\u81f4\u3002<\/p>\n
\u4f7f\u7528scikit-learn\u7684preprocessing.MinMaxScaler()<\/code>\u51fd\u6570\u5f52\u4e00\u5316\u6570\u636e<\/h2>\n
\u60a8\u53ef\u4ee5\u4f7f\u7528scikit-learn\u7684preprocessing.MinMaxScaler()<\/code>\u51fd\u6570\u901a\u8fc7\u5c06\u6570\u636e\u7f29\u653e\u5230\u4e00\u5b9a\u8303\u56f4\u6765\u5f52\u4e00\u5316\u6bcf\u4e2a\u7279\u5f81\u3002<\/p>\n
MinMaxScaler()<\/code>\u51fd\u6570\u4f1a\u5355\u72ec\u7f29\u653e\u6bcf\u4e2a\u7279\u5f81\uff0c\u4f7f\u503c\u5177\u6709\u7ed9\u5b9a\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\uff0c\u9ed8\u8ba4\u5206\u522b\u4e3a0\u548c1\u3002<\/p>\n
\u5c06\u7279\u5f81\u503c\u7f29\u653e\u52300\u52301\u4e4b\u95f4\u7684\u516c\u5f0f\u662f\uff1a<\/p>\n
\"\u7279\u5f81\u7f29\u653e\u516c\u5f0f\"<\/div>\n
\u4ece\u6bcf\u4e2a\u6570\u636e\u9879\u4e2d\u51cf\u53bb\u6700\u5c0f\u503c\uff0c\u7136\u540e\u5c06\u7ed3\u679c\u9664\u4ee5\u8303\u56f4\uff08\u5176\u4e2d\u8303\u56f4\u662f\u6700\u5927\u503c\u548c\u6700\u5c0f\u503c\u4e4b\u95f4\u7684\u5dee\uff09\u3002<\/p>\n
\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u4e86\u5982\u4f55\u4f7f\u7528MinMaxScaler()<\/code>\u51fd\u6570\u5f52\u4e00\u5316\u52a0\u5229\u798f\u5c3c\u4e9a\u4f4f\u623f\u6570\u636e\u96c6\uff1a<\/p>\n
minmax01.py<\/div>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> pandas as<\/span> pd\r\n\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\nscaler =<\/span> preprocessing.<\/span>MinMaxScaler(<\/span>)<\/span>\r\nd =<\/span> scaler.<\/span>fit_transform(<\/span>california_housing.<\/span>data)<\/span>\r\nscaled_df =<\/span> pd.<\/span>DataFrame(<\/span>d,<\/span> columns=<\/span>california_housing.<\/span>data.<\/span>columns)<\/span>\r\nprint<\/span>(<\/span>scaled_df)<\/span>\r\n<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c<\/p>\n
<\/code><\/pre>\n
MedInc HouseAge AveRooms … AveOccup Latitude Longitude
\n0 0.539668 0.784314 0.043512 … 0.001499 0.567481 0.211155
\n1 0.538027 0.392157 0.038224 … 0.001141 0.565356 0.212151
\n2 0.466028 1.000000 0.052756 … 0.001698 0.564293 0.210159
\n3 0.354699 1.000000 0.035241 … 0.001493 0.564293 0.209163
\n4 0.230776 1.000000 0.038534 … 0.001198 0.564293 0.209163
\n… … … … … … … …
\n20635 0.073130 0.470588 0.029769 … 0.001503 0.737513 0.324701
\n20636 0.141853 0.333333 0.037344 … 0.001956 0.738576 0.312749
\n20637 0.082764 0.313725 0.030904 … 0.001314 0.732200 0.311753
\n20638 0.094295 0.333333 0.031783 … 0.001152 0.732200 0.301793
\n20639 0.130253 0.294118 0.031252 … 0.001549 0.725824 0.309761
\n[20640 \u884c x 8 \u5217]<\/p>\n
<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c\u663e\u793a\uff0c\u6570\u503c\u5df2\u88ab\u7f29\u653e\u5230\u9ed8\u8ba4\u7684\u6700\u5c0f\u503c\u4e3a0\uff0c\u6700\u5927\u503c\u4e3a1\u3002<\/p>\n
\u60a8\u4e5f\u53ef\u4ee5\u4e3a\u7f29\u653e\u6307\u5b9a\u4e0d\u540c\u7684\u6700\u5c0f\u503c\u548c\u6700\u5927\u503c\u3002\u5728\u4ee5\u4e0b\u793a\u4f8b\u4e2d\uff0c\u6700\u5c0f\u503c\u4e3a0\uff0c\u6700\u5927\u503c\u4e3a2\uff1a<\/p>\n
minmax02.py<\/div>\n
\u8fd9\u662f\u6587\u7ae0\u300a\u5982\u4f55\u4f7f\u7528scikit-learn\u5728Python\u4e2d\u5f52\u4e00\u5316\u6570\u636e\u300b\u7684\u7b2c6\u90e8\u5206\uff08\u51716\u90e8\u5206\uff09\u3002<\/p>\n
from<\/span> sklearn import<\/span> preprocessing\r\nimport<\/span> pandas as<\/span> pd\r\n\r\nfrom<\/span> sklearn.<\/span>datasets import<\/span> fetch_california_housing\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\nscaler =<\/span> preprocessing.<\/span>MinMaxScaler(<\/span>feature_range=<\/span>(<\/span>0<\/span>,<\/span> 2<\/span>)<\/span><\/mark>)<\/span>\r\nd =<\/span> scaler.<\/span>fit_transform(<\/span>california_housing.<\/span>data)<\/span>\r\nscaled_df =<\/span> pd.<\/span>DataFrame(<\/span>d,<\/span> columns=<\/span>california_housing.<\/span>data.<\/span>columns)<\/span>\r\nprint<\/span>(<\/span>scaled_df)<\/span>\r\n<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c\u5982\u4e0b\uff1a<\/p>\n
         MedInc  HouseAge  AveRooms  ...  AveOccup  Latitude  Longitude\r\n0      1.079337  1.568627  0.087025  ...  0.002999  1.134963   0.422311\r\n1      1.076054  0.784314  0.076448  ...  0.002281  1.130712   0.424303\r\n2      0.932056  2.000000  0.105513  ...  0.003396  1.128587   0.420319\r\n3      0.709397  2.000000  0.070482  ...  0.002987  1.128587   0.418327\r\n4      0.461552  2.000000  0.077068  ...  0.002397  1.128587   0.418327\r\n...\r\n20635  0.146260  0.941176  0.059538  ...  0.003007  1.475027   0.649402\r\n20636  0.283706  0.666667  0.074688  ...  0.003912  1.477152   0.625498\r\n20637  0.165529  0.627451  0.061808  ...  0.002629  1.464400   0.623506\r\n20638  0.188591  0.666667  0.063565  ...  0.002303  1.464400   0.603586\r\n20639  0.260507  0.588235  0.062505  ...  0.003098  1.451647   0.619522\r\n\r\n[20640 rows x 8 columns]\r\n<\/code><\/pre>\n
\u8f93\u51fa\u7ed3\u679c\u663e\u793a\uff0c\u6240\u6709\u503c\u90fd\u88ab\u7f29\u653e\u5230\u6700\u5c0f\u503c\u4e3a0\uff0c\u6700\u5927\u503c\u4e3a2\u3002<\/p>\n
\u603b\u7ed3<\/h2>\n
\u5728\u672c\u6587\u4e2d\uff0c\u60a8\u5b66\u4e60\u4e86\u5982\u4f55\u4f7f\u7528scikit-learn\u7684\u4e24\u4e2a\u51fd\u6570\uff0c\u901a\u8fc7\u6309\u6837\u672c\uff08\u884c\uff09\u548c\u6309\u7279\u5f81\uff08\u5217\uff09\u4e24\u79cd\u4e0d\u540c\u65b9\u5f0f\u6765\u5f52\u4e00\u5316\u6570\u636e\u3002\u60a8\u53ef\u4ee5\u7ee7\u7eed\u5b66\u4e60\u5176\u4ed6\u673a\u5668\u5b66\u4e60\u4e3b\u9898\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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