{"id":70,"date":"2023-10-29T15:51:20","date_gmt":"2023-03-21T15:13:35","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/python%e3%81%a7%e3%81%ae%e6%90%8d%e5%a4%b1%e9%96%a2%e6%95%b0-%e7%b0%a1%e5%8d%98%e3%81%aa%e5%ae%9f%e8%a3%85\/"},"modified":"2025-07-31T13:48:36","modified_gmt":"2025-07-31T04:48:36","slug":"python%e3%81%a7%e3%81%ae%e6%90%8d%e5%a4%b1%e9%96%a2%e6%95%b0-%e7%b0%a1%e5%8d%98%e3%81%aa%e5%ae%9f%e8%a3%85","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/python%e3%81%a7%e3%81%ae%e6%90%8d%e5%a4%b1%e9%96%a2%e6%95%b0-%e7%b0%a1%e5%8d%98%e3%81%aa%e5%ae%9f%e8%a3%85\/","title":{"rendered":"Python\u3067\u306e\u640d\u5931\u95a2\u6570 – \u7c21\u5358\u306a\u5b9f\u88c5"},"content":{"rendered":"

\u640d\u5931\u95a2\u6570\u306fPython\u306b\u304a\u3044\u3066\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u91cd\u8981\u306a\u4e00\u90e8\u3067\u3059\u3002\u3053\u308c\u3089\u306e\u95a2\u6570\u306f\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3055\u308c\u305f\u51fa\u529b\u304c\u5b9f\u969b\u306e\u51fa\u529b\u3068\u3069\u308c\u3060\u3051\u7570\u306a\u308b\u304b\u3092\u6559\u3048\u3066\u304f\u308c\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u306f\u8907\u6570\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3088\u308a\u4eba\u6c17\u306e\u3042\u308b\u640d\u5931\u95a2\u6570\u306b\u3064\u3044\u3066\u898b\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4ee5\u4e0b\u306e4\u3064\u306e\u640d\u5931\u95a2\u6570\u306b\u3064\u3044\u3066\u8a71\u3057\u5408\u3044\u307e\u3059\u3002<\/p>\n

    \n
  1. \n
      \u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\uff08Mean Square Error\uff09<\/ol>\n<\/li>\n<\/ol>\n

       <\/p>\n

        \n
      1. \n
          \u5e73\u65b9\u6839\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\uff08Root Mean Square Error\uff09<\/ol>\n<\/li>\n<\/ol>\n

           <\/p>\n

            \n
          1. \n
              \u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\uff08Mean Absolute Error\uff09<\/ol>\n<\/li>\n<\/ol>\n

               <\/p>\n

                \u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\uff08Cross-Entropy Loss\uff09<\/ol>\n

                \u3053\u308c\u3089\u306e4\u3064\u306e\u640d\u5931\u95a2\u6570\u306e\u3046\u3061\u3001\u6700\u521d\u306e3\u3064\u306f\u56de\u5e30\u306b\u9069\u7528\u53ef\u80fd\u3067\u3042\u308a\u3001\u6700\u5f8c\u306e1\u3064\u306f\u5206\u985e\u30e2\u30c7\u30eb\u306e\u5834\u5408\u306b\u9069\u7528\u53ef\u80fd\u3067\u3059\u3002<\/p>\n

                Python\u3067\u640d\u5931\u95a2\u6570\u3092\u5b9f\u88c5\u3059\u308b<\/h2>\n

                \u3053\u308c\u3089\u306e\u640d\u5931\u95a2\u6570\u3092Python\u3067\u5b9f\u88c5\u3059\u308b\u65b9\u6cd5\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n

                1. \u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee(MSE)<\/h3>\n

                \u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\uff08MSE\uff09\u306f\u3001\u4e88\u6e2c\u5024\u3068\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u306e\u5dee\u306e2\u4e57\u306e\u5e73\u5747\u3068\u3057\u3066\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002\u6570\u5b66\u7684\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n

                \"Mean<\/div>\n

                MSE\u306ePython\u306e\u5b9f\u88c5\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\uff1a<\/p>\n

                import<\/span> numpy as<\/span> np\r\ndef<\/span> mean_squared_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n\r\n   diff =<\/span> pred -<\/span> act\r\n   differences_squared =<\/span> diff **<\/span> 2<\/span>\r\n   mean_diff =<\/span> differences_squared.<\/span>mean(<\/span>)<\/span>\r\n   \r\n   return<\/span> mean_diff\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\n\r\nprint<\/span>(<\/span>mean_squared_error(<\/span>act,<\/span>pred)<\/span>)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b:<\/p>\n
                0.04666666666666667\r\n<\/code><\/pre>\n
                sklearn\u306emean_squared_error\u3082\u4f7f\u3063\u3066\u3001MSE\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u4ee5\u4e0b\u306b\u305d\u306e\u95a2\u6570\u306e\u4f7f\u3044\u65b9\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n
                from<\/span> sklearn.<\/span>metrics import<\/span> mean_squared_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_squared_error(<\/span>act,<\/span> pred)<\/span>\r\n<\/code><\/pre>\n
                \u7d50\u679c\uff1a<\/p>\n
                0.04666666666666667\r\n<\/code><\/pre>\n
                2. \u30eb\u30fc\u30c8\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee (RMSE)<\/h3>\n
                \u5e73\u65b9\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\uff08RMSE\uff09\u306f\u3001\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee\u306e\u5e73\u65b9\u6839\u3067\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002\u6570\u5b66\u7684\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
                \"RMSE<\/div>\n
                RMSE\u306ePython\u5b9f\u88c5\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n
                import<\/span> numpy as<\/span> np\r\ndef<\/span> root_mean_squared_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n\r\n   diff =<\/span> pred -<\/span> act\r\n   differences_squared =<\/span> diff **<\/span> 2<\/span>\r\n   mean_diff =<\/span> differences_squared.<\/span>mean(<\/span>)<\/span>\r\n   rmse_val =<\/span> np.<\/span>sqrt(<\/span>mean_diff)<\/span>\r\n   return<\/span> rmse_val\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\n\r\nprint<\/span>(<\/span>root_mean_squared_error(<\/span>act,<\/span>pred)<\/span>)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b:<\/p>\n
                0.21602468994692867\r\n<\/code><\/pre>\n
                \u300cmean_squared_error\u300d\u3092\u4f7f\u7528\u3057\u3066\u3001RMSE\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u540c\u3058\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001RMSE\u3092\u5b9f\u88c5\u3059\u308b\u65b9\u6cd5\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
                from<\/span> sklearn.<\/span>metrics import<\/span> mean_squared_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_squared_error(<\/span>act,<\/span> pred,<\/span> squared =<\/span> False<\/span>)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b:<\/p>\n
                0.21602468994692867\r\n<\/code><\/pre>\n
                \u3082\u3057\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u300csquared\u300d\u304cTrue\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u3001\u95a2\u6570\u306fMSE\u306e\u5024\u3092\u8fd4\u3057\u307e\u3059\u3002False\u306b\u8a2d\u5b9a\u3055\u308c\u3066\u3044\u308b\u5834\u5408\u3001\u95a2\u6570\u306fRMSE\u306e\u5024\u3092\u8fd4\u3057\u307e\u3059\u3002<\/p>\n
                3. \u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\uff08MAE\uff09<\/h3>\n
                Mean Absolute Error (MAE)\u306f\u3001\u4e88\u6e2c\u5024\u3068\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u306e\u7d76\u5bfe\u5024\u306e\u5dee\u306e\u5e73\u5747\u3068\u3057\u3066\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002\u6570\u5b66\u7684\u306b\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u8868\u73fe\u3067\u304d\u307e\u3059\u3002<\/p>\n
                \"MAE<\/div>\n
                MAE\u306ePython\u5b9f\u88c5\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059:<\/p>\n
                import<\/span> numpy as<\/span> np\r\ndef<\/span> mean_absolute_error<\/span>(<\/span>act,<\/span> pred)<\/span>:<\/span>\r\n    diff =<\/span> pred -<\/span> act\r\n    abs_diff =<\/span> np.<\/span>absolute(<\/span>diff)<\/span>\r\n    mean_diff =<\/span> abs_diff.<\/span>mean(<\/span>)<\/span>\r\n    return<\/span> mean_diff\r\n\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_absolute_error(<\/span>act,<\/span>pred)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b\uff1a<\/p>\n
                0.20000000000000004\r\n<\/code><\/pre>\n
                \u3042\u306a\u305f\u306fsklearn\u306emean_absolute_error\u3082\u4f7f\u7528\u3057\u3066MAE\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n
                from<\/span> sklearn.<\/span>metrics import<\/span> mean_absolute_error\r\nact =<\/span> np.<\/span>array(<\/span>[<\/span>1.1<\/span>,<\/span>2<\/span>,<\/span>1.7<\/span>]<\/span>)<\/span>\r\npred =<\/span> np.<\/span>array(<\/span>[<\/span>1<\/span>,<\/span>1.7<\/span>,<\/span>1.5<\/span>]<\/span>)<\/span>\r\nmean_absolute_error(<\/span>act,<\/span> pred)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b:<\/p>\n
                0.20000000000000004\r\n<\/code><\/pre>\n
                Python\u3067\u306e\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u95a2\u6570<\/h3>\n
                \u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u306f\u3001\u30cd\u30ac\u30c6\u30a3\u30d6\u5bfe\u6570\u5c24\u5ea6\u3068\u3082\u79f0\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u4e3b\u306b\u5206\u985e\u554f\u984c\u3067\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u5206\u985e\u554f\u984c\u3068\u306f\u3001\u4f8b\u30922\u3064\u4ee5\u4e0a\u306e\u30af\u30e9\u30b9\u306e\u3044\u305a\u308c\u304b\u306b\u5206\u985e\u3059\u308b\u554f\u984c\u3067\u3059\u3002<\/p>\n
                \u30d0\u30a4\u30ca\u30ea\u5206\u985e\u554f\u984c\u306e\u5834\u5408\u3001\u30a8\u30e9\u30fc\u306e\u8a08\u7b97\u65b9\u6cd5\u306b\u3064\u3044\u3066\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
                \u72ac\u3068\u732b\u306e\u533a\u5225\u3092\u3057\u3088\u3046\u3068\u3059\u308b\u30e2\u30c7\u30eb\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
                \u4ee5\u4e0b\u306f\u30a8\u30e9\u30fc\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306ePython\u306e\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n
                from<\/span> sklearn.<\/span>metrics import<\/span> log_loss\r\nlog_loss(<\/span>[<\/span>\"Dog\"<\/span>,<\/span> \"Cat\"<\/span>,<\/span> \"Cat\"<\/span>,<\/span> \"Dog\"<\/span>]<\/span>,<\/span>[<\/span>[<\/span>.1<\/span>,<\/span> .9<\/span>]<\/span>,<\/span> [<\/span>.9<\/span>,<\/span> .1<\/span>]<\/span>,<\/span> [<\/span>.8<\/span>,<\/span> .2<\/span>]<\/span>,<\/span> [<\/span>.35<\/span>,<\/span> .65<\/span>]<\/span>]<\/span>)<\/span>\r\n<\/code><\/pre>\n
                \u51fa\u529b:
                \n\u51fa\u529b\u7d50\u679c:<\/p>\n
                0.21616187468057912\r\n<\/code><\/pre>\n
                \u79c1\u305f\u3061\u306f\u3001scikit-learn\u306elog_loss\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n
                \u95a2\u6570\u306e\u547c\u3073\u51fa\u3057\u6642\u306e\u6700\u521d\u306e\u5f15\u6570\u306f\u3001\u5404\u5165\u529b\u306b\u5bfe\u3059\u308b\u6b63\u3057\u3044\u30af\u30e9\u30b9\u30e9\u30d9\u30eb\u306e\u30ea\u30b9\u30c8\u3067\u3059\u30022\u756a\u76ee\u306e\u5f15\u6570\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3055\u308c\u305f\u78ba\u7387\u306e\u30ea\u30b9\u30c8\u3067\u3059\u3002<\/p>\n
                \u78ba\u7387\u306f\u4ee5\u4e0b\u306e\u5f62\u5f0f\u3067\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/p>\n
                [P(dog), P(cat)]\r\n<\/code><\/pre>\n
                \u7d50\u8ad6<\/h2>\n
                \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u306f\u3001Python\u3067\u306e\u640d\u5931\u95a2\u6570\u306b\u3064\u3044\u3066\u3067\u3057\u305f\u3002\u56de\u5e30\u304a\u3088\u3073\u5206\u985e\u306e\u554f\u984c\u306b\u5bfe\u3057\u3066\u3055\u307e\u3056\u307e\u306a\u640d\u5931\u95a2\u6570\u3092\u30ab\u30d0\u30fc\u3057\u307e\u3057\u305f\u3002\u79c1\u305f\u3061\u3068\u4e00\u7dd2\u306b\u5b66\u3076\u3053\u3068\u304c\u697d\u3057\u304b\u3063\u305f\u3068\u9858\u3063\u3066\u3044\u307e\u3059\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"
                \u640d\u5931\u95a2\u6570\u306fPython\u306b\u304a\u3044\u3066\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u91cd\u8981\u306a\u4e00\u90e8\u3067\u3059\u3002\u3053\u308c\u3089\u306e\u95a2\u6570\u306f\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u3055\u308c\u305f\u51fa\u529b\u304c\u5b9f\u969b\u306e\u51fa\u529b\u3068\u3069\u308c\u3060\u3051\u7570\u306a\u308b\u304b\u3092\u6559\u3048\u3066\u304f\u308c\u307e\u3059\u3002 \u3053\u306e\u5dee\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u306f\u8907\u6570\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3088\u308a\u4eba\u6c17\u306e […]<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[31,26],"class_list":["post-70","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-python","tag-26"],"yoast_head":"\nPython\u3067\u306e\u640d\u5931\u95a2\u6570 - 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