{"id":10,"date":"2022-12-15T04:24:20","date_gmt":"2023-07-28T06:51:33","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/python%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6scikit-learn%e3%82%92%e4%bd%bf%e3%81%a3%e3%81%a6%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95\/"},"modified":"2025-07-30T06:21:50","modified_gmt":"2025-07-29T21:21:50","slug":"python%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6scikit-learn%e3%82%92%e4%bd%bf%e3%81%a3%e3%81%a6%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/python%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6scikit-learn%e3%82%92%e4%bd%bf%e3%81%a3%e3%81%a6%e3%83%87%e3%83%bc%e3%82%bf%e3%82%92%e6%ad%a3%e8%a6%8f%e5%8c%96%e3%81%99%e3%82%8b%e6%96%b9%e6%b3%95\/","title":{"rendered":"Python\u3092\u4f7f\u7528\u3057\u3066scikit-learn\u3092\u4f7f\u3063\u3066\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

\u306f\u3058\u3081\u306b<\/h3>\n

\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001scikit-learn\uff08\u307e\u305f\u306fsklearn\u3068\u3082\u547c\u3070\u308c\u308b\uff09\u3092\u4f7f\u7528\u3057\u3066\u3001Python\u3067\u30c7\u30fc\u30bf\u306e\u6b63\u898f\u5316\u3092\u884c\u3046\u3044\u304f\u3064\u304b\u306e\u7570\u306a\u308b\u65b9\u6cd5\u3092\u8a66\u3057\u3066\u307f\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u3068\u3001\u30c7\u30fc\u30bf\u306e\u30b9\u30b1\u30fc\u30eb\u304c\u5909\u66f4\u3055\u308c\u307e\u3059\u3002\u30c7\u30fc\u30bf\u306f\u4e00\u822c\u7684\u306b0\u304b\u30891\u306e\u7bc4\u56f2\u306b\u518d\u30b9\u30b1\u30fc\u30eb\u3055\u308c\u307e\u3059\u3002\u306a\u305c\u306a\u3089\u3001\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f\u3001\u7570\u306a\u308b\u7279\u5fb4\u304c\u3088\u308a\u5c0f\u3055\u306a\u30b9\u30b1\u30fc\u30eb\u3067\u3042\u308b\u5834\u5408\u306b\u3001\u3088\u308a\u826f\u3044\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u767a\u63ee\u3057\u305f\u308a\u3001\u3088\u308a\u901f\u304f\u53ce\u675f\u3057\u305f\u308a\u3059\u308b\u50be\u5411\u304c\u3042\u308b\u305f\u3081\u3067\u3059\u3002\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u8a13\u7df4\u3059\u308b\u524d\u306b\u3001\u307e\u305a\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u306e\u304c\u4e00\u822c\u7684\u306a\u6163\u884c\u3067\u3042\u308a\u3001\u53ef\u80fd\u6027\u3068\u3057\u3066\u3088\u308a\u826f\u3044\u3001\u3088\u308a\u901f\u3044\u7d50\u679c\u304c\u5f97\u3089\u308c\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u6b63\u898f\u5316\u306b\u3088\u308a\u3001\u8a13\u7df4\u30d7\u30ed\u30bb\u30b9\u3082\u7279\u5fb4\u306e\u30b9\u30b1\u30fc\u30eb\u306b\u5bfe\u3057\u3066\u611f\u5ea6\u304c\u4f4e\u304f\u306a\u308a\u3001\u8a13\u7df4\u5f8c\u306e\u4fc2\u6570\u3082\u3088\u308a\u826f\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n

\u7279\u5fb4\u306e\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u306b\u3088\u3063\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u9069\u3057\u305f\u7279\u5fb4\u306b\u306a\u308b\u30d7\u30ed\u30bb\u30b9\u306f\u3001\u7279\u5fb4\u306e\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3068\u547c\u3070\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Python\u30d0\u30fc\u30b8\u30e7\u30f33.9.13\u3068scikit-learn\u30d0\u30fc\u30b8\u30e7\u30f31.0.2\u3092\u4f7f\u7528\u3057\u3066\u30c6\u30b9\u30c8\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n

\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u305f\u3081\u306b\u3001scikit-learn\u306epreprocessing.normalize()\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3002<\/h2>\n

\u914d\u5217\u306e\u3088\u3046\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u305f\u3081\u306b\u3001scikit-learn\u306epreprocessing.normalize()\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/p>\n

normalize\uff08\u6b63\u898f\u5316\uff09\u95a2\u6570\u306f\u3001\u30d9\u30af\u30c8\u30eb\u3092\u500b\u5225\u306b\u5358\u4f4d\u30ce\u30eb\u30e0\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3057\u3001\u30d9\u30af\u30c8\u30eb\u306e\u9577\u3055\u304c1\u306b\u306a\u308b\u3088\u3046\u306b\u3057\u307e\u3059\u3002normalize\uff08\u6b63\u898f\u5316\uff09\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30ce\u30eb\u30e0\u306fL2\u3067\u3042\u308a\u3001\u30e6\u30fc\u30af\u30ea\u30c3\u30c9\u30ce\u30eb\u30e0\u3068\u3082\u547c\u3070\u308c\u3066\u3044\u307e\u3059\u3002L2\u30ce\u30eb\u30e0\u306e\u5f0f\u306f\u3001\u5404\u5024\u306e\u4e8c\u4e57\u306e\u5408\u8a08\u306e\u5e73\u65b9\u6839\u3067\u3059\u3002normalize\uff08\u6b63\u898f\u5316\uff09\u95a2\u6570\u306e\u4f7f\u7528\u306b\u3088\u308a\u3001\u5024\u306f0\u304b\u30891\u306e\u9593\u306b\u306a\u308a\u307e\u3059\u304c\u3001\u5024\u3092\u5358\u306b0\u304b\u30891\u306e\u7bc4\u56f2\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u306e\u3068\u306f\u7570\u306a\u308a\u307e\u3059\u3002<\/p>\n

normalize()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u914d\u5217\u3092\u6b63\u898f\u5316\u3059\u308b<\/h3>\n

NumPy\u306enormalize()\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3001\u4e00\u6b21\u5143\u306eNumPy\u914d\u5217\u3092\u6b63\u898f\u5316\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

sklearn.preprocessing\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n

from<\/span> sklearn import<\/span> preprocessing\r\n<\/code><\/pre>\n
NumPy\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u3001\u914d\u5217\u3092\u4f5c\u6210\u3059\u308b\u3002<\/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
\uff11\u6b21\u5143\u306e\u914d\u5217\u306e\u5834\u5408\u3001normalize()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u884c\u3054\u3068\u306b\u6b63\u898f\u5316\u3057\u307e\u3059\u3002<\/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
\u5b8c\u5168\u306a\u4f8b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3001normalize\uff08\uff09\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066NumPy\u914d\u5217\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\u3092\u30c7\u30e2\u30f3\u30b9\u30c8\u30ec\u30fc\u30b7\u30e7\u30f3\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
norm_numpy.py\u3092\u65e5\u672c\u8a9e\u3067\u8ff0\u3079\u308b\u3068\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059:<\/div>\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
\u51fa\u529b\u7d50\u679c\u306f\uff1a<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
[[0.11785113 0.1767767 0.29462783 0.35355339 0.41247896 0.23570226 0.47140452 0.41247896 0.35355339]]<\/p>\n
<\/code><\/pre>\n
\u51fa\u529b\u306f\u5168\u3066\u306e\u5024\u304c0\u304b\u30891\u306e\u7bc4\u56f2\u5185\u306b\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u51fa\u529b\u306e\u5404\u5024\u3092\u4e8c\u4e57\u3057\u3001\u305d\u308c\u3092\u5408\u8a08\u3059\u308b\u3068\u3001\u7d50\u679c\u306f1\u3001\u307e\u305f\u306f1\u306b\u975e\u5e38\u306b\u8fd1\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306enormalize()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30ab\u30e9\u30e0\u3092\u6b63\u898f\u5316\u3059\u308b\u3053\u3068\u3067\u3001\u30ab\u30e9\u30e0\u306e\u5024\u3092\u6a19\u6e96\u5316\u3057\u307e\u3059\u3002<\/h3>\n
\u30d1\u30f3\u30c0\u306e\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3067\u306f\u3001\u7279\u5fb4\u91cf\u306f\u5217\u3067\u3042\u308a\u3001\u30b5\u30f3\u30d7\u30eb\u306f\u884c\u3067\u3059\u3002\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u306e\u5217\u3092NumPy\u914d\u5217\u306b\u5909\u63db\u3057\u3001\u305d\u306e\u914d\u5217\u5185\u306e\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u304a\u3088\u3073\u7d9a\u304f\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306e\u4f8b\u306f\u3001\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u4f4f\u5b85\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u4f8b\u30b3\u30fc\u30c9\u306e\u6700\u521d\u306e\u90e8\u5206\u306f\u3001\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u307f\u3001\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aac\u660e\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/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# create the DataFrame<\/span>\r\ncalifornia_housing =<\/span> fetch_california_housing(<\/span>as_frame=<\/span>True<\/span>)<\/span>\r\n\r\n# print the dataset description<\/span>\r\nprint<\/span>(<\/span>california_housing.<\/span>DESCR)<\/span>\r\n<\/code><\/pre>\n
\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u306e\u4f4f\u5b85\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3059\u308b\u305f\u3081\u306b\u3001as_frame\u30d1\u30e9\u30e1\u30fc\u30bf\u3092True\u306b\u8a2d\u5b9a\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aac\u660e\u306b\u306f\u4ee5\u4e0b\u306e\u629c\u7c8b\u304c\u542b\u307e\u308c\u3066\u304a\u308a\u3001\u6b63\u898f\u5316\u3059\u308b\u7279\u5fb4\u3092\u9078\u3076\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
.. _california_housing_dataset: California Housing dataset ————————– **Data Set Characteristics:** :Number of Instances: 20640 :Number of Attributes: 8 numeric, predictive attributes and the target :Attribute Information: – MedInc median income in block group – HouseAge median house age in block group – AveRooms average number of rooms per household – AveBedrms average number of bedrooms per household – Population block group population – AveOccup average number of household members – Latitude block group latitude – Longitude block group longitude …<\/p>\n
<\/code><\/pre>\n
\u6b21\u306b\u3001\u5217\uff08\u7279\u5fb4\uff09\u3092\u914d\u5217\u306b\u5909\u63db\u3057\u3001\u305d\u308c\u3092\u5370\u5237\u3057\u307e\u3059\u3002\u3053\u306e\u4f8b\u3067\u306f\u3001HouseAge\u5217\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
x_array =<\/span> np.<\/span>array(<\/span>california_housing[<\/span>'HouseAge'<\/span>]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"HouseAge array: \"<\/span>,<\/span>x_array)<\/span>\r\n<\/code><\/pre>\n
\u6700\u5f8c\u306b\u3001normalize()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3057\u3001\u7d50\u679c\u306e\u914d\u5217\u3092\u51fa\u529b\u3057\u307e\u3059\u3002<\/p>\n
normalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"Normalized HouseAge array: \"<\/span>,<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n
\u30ce\u30fc\u30de\u30e9\u30a4\u30ba\uff08normalize\uff09\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30d5\u30a3\u30fc\u30c1\u30e3\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3059\u305f\u3081\u306b\u3001\u5b8c\u5168\u306a\u4f8b\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u30cd\u30a4\u30c6\u30a3\u30d6\u306a\u65e5\u672c\u8a9e\u3067\u4ee5\u4e0b\u3092\u8a00\u3044\u63db\u3048\u307e\u3059\u30021\u3064\u306e\u9078\u629e\u80a2\u306e\u307f\u3067\u3059\uff1a
\nnorm_feature.py \u2192 \u6b63\u898f\u5316\u7279\u5fb4\u91cf.py<\/div>\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>\"HouseAge array: \"<\/span>,<\/span>x_array)<\/span>\r\n\r\nnormalized_arr =<\/span> preprocessing.<\/span>normalize(<\/span>[<\/span>x_array]<\/span>)<\/span>\r\nprint<\/span>(<\/span>\"Normalized HouseAge array: \"<\/span>,<\/span>normalized_arr)<\/span>\r\n<\/code><\/pre>\n
\u51fa\u529b\u306f\uff1a<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
HouseAge array: [41. 21. 52. … 17. 18. 16.] Normalized HouseAge array: [[0.00912272 0.00467261 0.01157028 … 0.00378259 0.0040051 0.00356009]]<\/p>\n
<\/code><\/pre>\n
\u4ee5\u4e0b\u306e\u51fa\u529b\u306f\u3001normalize() \u95a2\u6570\u304c\u4e2d\u592e\u5024\u4f4f\u5b85\u5e74\u9f62\u306e\u914d\u5217\u3092\u5909\u66f4\u3057\u3001\u5024\u306e\u4e8c\u4e57\u548c\u306e\u5e73\u65b9\u6839\u304c1\u306b\u7b49\u3057\u304f\u306a\u308b\u3088\u3046\u306b\u3057\u305f\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u3064\u307e\u308a\u3001L2\u30ce\u30eb\u30e0\u3092\u4f7f\u7528\u3057\u3066\u5024\u304c\u5358\u4f4d\u9577\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n
\u300cnormalize()\u300d\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u884c\u307e\u305f\u306f\u5217\u3054\u3068\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u3002<\/h3>\n
\u7279\u5fb4\u91cf\u307e\u305f\u306f\u5217\u3092\u914d\u5217\u306b\u5909\u63db\u305b\u305a\u306b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u5834\u5408\u3001\u30c7\u30fc\u30bf\u306f\u884c\u3054\u3068\u306b\u6b63\u898f\u5316\u3055\u308c\u307e\u3059\u3002normalize()\u95a2\u6570\u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u8ef8\u306f1\u3067\u3042\u308a\u3001\u5404\u30b5\u30f3\u30d7\u30eb\u307e\u305f\u306f\u884c\u304c\u6b63\u898f\u5316\u3055\u308c\u307e\u3059\u3002<\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u8ef8\u3092\u4f7f\u7528\u3057\u3066California Housing\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u30ce\u30fc\u30e0\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30f3\u30d7\u30eb.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\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
\u51fa\u529b\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059:<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
MedInc HouseAge AveRooms … AveOccup Latitude Longitude 0 0.023848 0.117447 0.020007 … 0.007321 0.108510 -0.350136 1 0.003452 0.008734 0.002594 … 0.000877 0.015745 -0.050829 2 0.014092 0.100971 0.016093 … 0.005441 0.073495 -0.237359 3 0.009816 0.090449 0.010119 … 0.004432 0.065837 -0.212643 4 0.006612 0.089394 0.010799 … 0.003750 0.065069 -0.210162 … … … … … … … … 20635 0.001825 0.029242 0.005902 … 0.002995 0.046179 -0.141637 20636 0.006753 0.047539 0.016147 … 0.008247 0.104295 -0.320121 20637 0.001675 0.016746 0.005128 … 0.002291 0.038840 -0.119405 20638 0.002483 0.023932 0.007086 … 0.002823 0.052424 -0.161300 20639 0.001715 0.011486 0.003772 … 0.001879 0.028264 -0.087038 [20640 rows x 8 columns]<\/p>\n
<\/code><\/pre>\n
\u51fa\u529b\u306f\u3001\u5404\u30b5\u30f3\u30d7\u30eb\u3054\u3068\u306b\u6b63\u898f\u5316\u3055\u308c\u308b\u3088\u3046\u306b\u3001\u884c\u65b9\u5411\u306b\u5024\u304c\u6b63\u898f\u5316\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u305f\u3060\u3057\u3001\u8ef8\u3092\u6307\u5b9a\u3059\u308b\u3053\u3068\u3067\u7279\u5fb4\u306b\u3088\u308b\u6b63\u898f\u5316\u304c\u53ef\u80fd\u3067\u3059\u3002<\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001feature\u306b\u5bfe\u3057\u3066axis=0\u3092\u4f7f\u7528\u3057\u3066California Housing\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u300cnorm_dataset_feature.py\u300d\u3068\u3044\u3046\u30d5\u30a1\u30a4\u30eb\u3092\u65e5\u672c\u8a9e\u3067\u8a00\u3044\u63db\u3048\u308b\u65b9\u6cd5\u306f\u3001\u6b21\u306e\u901a\u308a\u3067\u3059\uff1a\u300c\u6b63\u898f\u5316\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7279\u5fb4.py\u300d<\/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\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
\u51fa\u529b\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
MedInc HouseAge AveRooms … AveOccup Latitude Longitude 0 0.013440 0.009123 0.008148 … 0.001642 0.007386 -0.007114 1 0.013401 0.004673 0.007278 … 0.001356 0.007383 -0.007114 2 0.011716 0.011570 0.009670 … 0.001801 0.007381 -0.007115 3 0.009110 0.011570 0.006787 … 0.001638 0.007381 -0.007116 4 0.006209 0.011570 0.007329 … 0.001402 0.007381 -0.007116 … … … … … … … … 20635 0.002519 0.005563 0.005886 … 0.001646 0.007698 -0.007048 20636 0.004128 0.004005 0.007133 … 0.002007 0.007700 -0.007055 20637 0.002744 0.003783 0.006073 … 0.001495 0.007689 -0.007056 20638 0.003014 0.004005 0.006218 … 0.001365 0.007689 -0.007061 20639 0.003856 0.003560 0.006131 … 0.001682 0.007677 -0.007057 [20640 rows x 8 columns]<\/p>\n
<\/code><\/pre>\n
\u51fa\u529b\u3092\u8abf\u3079\u308b\u3068\u3001HouseAge\u5217\u306e\u7d50\u679c\u304c\u3001\u4ee5\u524d\u306e\u4f8b\u3067HouseAge\u5217\u3092\u914d\u5217\u306b\u5909\u63db\u3057\u3001\u6b63\u898f\u5316\u3057\u305f\u3068\u304d\u306e\u51fa\u529b\u3068\u4e00\u81f4\u3059\u308b\u3053\u3068\u306b\u6c17\u4ed8\u304f\u3067\u3057\u3087\u3046\u3002<\/p>\n
scikit-learn\u306epreprocessing.MinMaxScaler()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c7\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u3002<\/h2>\n
\u5404\u7279\u5fb4\u91cf\u3092\u6b63\u898f\u5316\u3059\u308b\u305f\u3081\u306b\u3001scikit-learn\u306epreprocessing.MinMaxScaler()\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u30c7\u30fc\u30bf\u3092\u7bc4\u56f2\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u3067\u3001\u6b63\u898f\u5316\u304c\u884c\u308f\u308c\u307e\u3059\u3002<\/p>\n
MinMaxScaler()\u95a2\u6570\u306f\u3001\u5404\u30d5\u30a3\u30fc\u30c1\u30e3\u3092\u500b\u5225\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3057\u3001\u5024\u3092\u6307\u5b9a\u3057\u305f\u6700\u5c0f\u5024\u3068\u6700\u5927\u5024\u306b\u8abf\u6574\u3057\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u306f\u3001\u6700\u5c0f\u5024\u306f0\u3001\u6700\u5927\u5024\u306f1\u3067\u3059\u3002<\/p>\n
\u7279\u5fb4\u5024\u30920\u304b\u30891\u306e\u9593\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3059\u308b\u305f\u3081\u306e\u5f0f\u306f\u6b21\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\"formula<\/div>\n
\u5404\u30a8\u30f3\u30c8\u30ea\u30fc\u304b\u3089\u6700\u5c0f\u5024\u3092\u5f15\u304d\u3001\u305d\u306e\u7d50\u679c\u3092\u7bc4\u56f2\u3067\u9664\u7b97\u3057\u307e\u3059\u3002\u7bc4\u56f2\u3068\u306f\u3001\u6700\u5927\u5024\u3068\u6700\u5c0f\u5024\u306e\u5dee\u3067\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u4f8b\u306f\u3001MinMaxScaler()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30ab\u30ea\u30d5\u30a9\u30eb\u30cb\u30a2\u4f4f\u5b85\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
minmax01.py\u3092\u65e5\u672c\u8a9e\u3067\u81ea\u7136\u306b\u8a00\u3044\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002 1\u3064\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u3060\u3051\u3067\u3088\u3044\u3067\u3059\u3002
\n\u300cminmax01.py\u300d<\/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
\u51fa\u529b\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n
<\/code><\/pre>\n
Output<\/div>\n
<\/code><\/pre>\n
MedInc HouseAge AveRooms … AveOccup Latitude Longitude 0 0.539668 0.784314 0.043512 … 0.001499 0.567481 0.211155 1 0.538027 0.392157 0.038224 … 0.001141 0.565356 0.212151 2 0.466028 1.000000 0.052756 … 0.001698 0.564293 0.210159 3 0.354699 1.000000 0.035241 … 0.001493 0.564293 0.209163 4 0.230776 1.000000 0.038534 … 0.001198 0.564293 0.209163 … … … … … … … … 20635 0.073130 0.470588 0.029769 … 0.001503 0.737513 0.324701 20636 0.141853 0.333333 0.037344 … 0.001956 0.738576 0.312749 20637 0.082764 0.313725 0.030904 … 0.001314 0.732200 0.311753 20638 0.094295 0.333333 0.031783 … 0.001152 0.732200 0.301793 20639 0.130253 0.294118 0.031252 … 0.001549 0.725824 0.309761 [20640 rows x 8 columns]<\/p>\n
<\/code><\/pre>\n
\u51fa\u529b\u306f\u3001\u5024\u304c\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u6700\u5c0f\u50240\u3068\u6700\u5927\u50241\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u4ee5\u4e0b\u306e\u4f8b\u3067\u306f\u3001\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u306e\u6700\u5c0f\u5024\u30920\u3001\u6700\u5927\u5024\u30922\u306b\u6307\u5b9a\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n
\u30df\u30f3\u30de\u30c3\u30af\u30b902.py\u3092\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8a00\u3044\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002<\/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>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
\u51fa\u529b\u306f\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
\u51fa\u529b\u306f\u3001\u5024\u304c\u6700\u5c0f\u50240\u3068\u6700\u5927\u50242\u306b\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
\u7d50\u8ad6<\/h2>\n
\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001\u30b5\u30f3\u30d7\u30eb\uff08\u884c\uff09\u3068\u7279\u5fb4\u91cf\uff08\u5217\uff09\u306b\u3088\u3063\u3066\u30c7\u30fc\u30bf\u3092\u7570\u306a\u308b\u65b9\u6cd5\u3067\u6b63\u898f\u5316\u3059\u308b\u305f\u3081\u306b\u3001scikit-learn\u306e2\u3064\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u307e\u3057\u305f\u3002\u4ed6\u306e\u6a5f\u68b0\u5b66\u7fd2\u306e\u30c8\u30d4\u30c3\u30af\u306b\u3064\u3044\u3066\u5b66\u7fd2\u3092\u7d9a\u3051\u307e\u3057\u3087\u3046\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
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