{"id":3006,"date":"2023-07-19T15:56:18","date_gmt":"2023-07-19T15:56:18","guid":{"rendered":"https:\/\/statorials.org\/ja\/sklearn-%e7%b7%9a%e5%bd%a2%e5%9b%9e%e5%b8%b0%e3%81%ae%e6%a6%82%e8%a6%81\/"},"modified":"2023-07-19T15:56:18","modified_gmt":"2023-07-19T15:56:18","slug":"sklearn-%e7%b7%9a%e5%bd%a2%e5%9b%9e%e5%b8%b0%e3%81%ae%e6%a6%82%e8%a6%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/sklearn-%e7%b7%9a%e5%bd%a2%e5%9b%9e%e5%b8%b0%e3%81%ae%e6%a6%82%e8%a6%81\/","title":{"rendered":"Scikit-learn \u304b\u3089\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u53d6\u5f97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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Python \u3067scikit-learn<\/a>\u3092\u4f7f\u7528\u3057\u3066\u4f5c\u6210\u3055\u308c\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u62bd\u51fa\u3057\u305f\u3044\u5834\u5408\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u6b8b\u5ff5\u306a\u304c\u3089\u3001scikit-learn \u306f\u4e00\u822c\u306b\u4e88\u6e2c\u76ee\u7684<\/a>\u3067\u306e\u307f\u4f7f\u7528\u3055\u308c\u308b\u305f\u3081\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u5206\u6790\u3059\u308b\u305f\u3081\u306e\u7d44\u307f\u8fbc\u307f\u95a2\u6570\u306f\u3042\u307e\u308a\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u305b\u3093\u3002<\/span><\/p>\n

\u3057\u305f\u304c\u3063\u3066\u3001Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u53d6\u5f97\u3057\u305f\u3044\u5834\u5408\u306f\u30012 \u3064\u306e\u30aa\u30d7\u30b7\u30e7\u30f3\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

1.<\/strong> scikit-learn \u306e\u9650\u5b9a\u3055\u308c\u305f\u6a5f\u80fd\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

2.<\/strong>\u4ee3\u308f\u308a\u306b\u7d71\u8a08\u30e2\u30c7\u30eb<\/a>\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u6b21\u306e\u4f8b\u306f\u3001\u6b21\u306e pandas DataFrame \u3067\u5404\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\ndf = pd. DataFrame<\/span> ({' x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4],\n                   ' y<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n#view first five rows of DataFrame\n<\/span>df. head<\/span> ()\n\n       x1 x2 y\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72\n<\/span><\/span><\/strong><\/pre>\n
\u65b9\u6cd5 1: Scikit-Learn \u304b\u3089\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u53d6\u5f97\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001scikit-learn \u3092\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30<\/a>\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df[[' x1<\/span> ', ' x2<\/span> ']], df. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n<\/span><\/span><\/strong><\/pre>\n
 #display regression coefficients and R-squared value of model<\/span>\nprint<\/span> (model. intercept_<\/span> , model. coef_<\/span> , model. score<\/span> (X, y))\n\n70.4828205704 [5.7945 -1.1576] 0.766742556527\n<\/span><\/span><\/strong><\/pre>\n
\u3053\u306e\u51fa\u529b\u3092\u4f7f\u7528\u3057\u3066\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u65b9\u7a0b\u5f0f\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 y = 70.48 + 5.79x 1<\/sub> \u2013 1.16x 2<\/sub><\/span><\/p>\n
\u30e2\u30c7\u30eb\u306e R 2<\/sup>\u5024\u304c 76.67 \u3067\u3042\u308b\u3053\u3068\u3082\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306e\u5909\u52d5\u306e76.67%<\/strong>\u304c\u30e2\u30c7\u30eb\u5185\u306e 2 \u3064\u306e\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u65b9\u6cd5 2: Statsmodels \u304b\u3089\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u53d6\u5f97\u3059\u308b<\/strong><\/span><\/h3>\n
Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u62bd\u51fa\u3059\u308b\u5834\u5408\u306f\u3001 statsmodels<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3059\u308b\u306e\u304c\u6700\u9069\u3067\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u3053\u306e\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066\u524d\u306e\u4f8b\u3068\u540c\u3058\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3001\u30e2\u30c7\u30eb\u306e\u6982\u8981\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df[' y<\/span> ']\n\n#define predictor variables\n<\/span>x = df[[' x1<\/span> ', ' x2<\/span> ']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: y R-squared: 0.767\nModel: OLS Adj. R-squared: 0.708\nMethod: Least Squares F-statistic: 13.15\nDate: Fri, 01 Apr 2022 Prob (F-statistic): 0.00296\nTime: 11:10:16 Log-Likelihood: -31.191\nNo. Comments: 11 AIC: 68.38\nDf Residuals: 8 BIC: 69.57\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 70.4828 3.749 18.803 0.000 61.839 79.127\nx1 5.7945 1.132 5.120 0.001 3.185 8.404\nx2 -1.1576 1.065 -1.087 0.309 -3.613 1.298\n==================================================== ============================\nOmnibus: 0.198 Durbin-Watson: 1.240\nProb(Omnibus): 0.906 Jarque-Bera (JB): 0.296\nSkew: -0.242 Prob(JB): 0.862\nKurtosis: 2.359 Cond. No. 10.7\n==================================================== ============================\n<\/strong><\/span><\/pre>\n
\u56de\u5e30\u4fc2\u6570\u3068 R \u4e8c\u4e57\u5024\u306f scikit-learn \u306b\u3088\u3063\u3066\u8a08\u7b97\u3055\u308c\u305f\u3082\u306e\u3068\u4e00\u81f4\u3057\u307e\u3059\u304c\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u306f\u4ed6\u306b\u3082\u5927\u91cf\u306e\u6709\u7528\u306a\u30e1\u30c8\u30ea\u30af\u30b9\u304c\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
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\u3053\u306e\u7d50\u679c\u306f\u6709\u7528\u3067\u3059\u304c\u3001\u30e2\u30c7\u30eb\u306e\u5168\u4f53\u7684\u306a F \u7d71\u8a08\u91cf<\/a>\u3001\u500b\u3005\u306e\u56de\u5e30\u4fc2\u6570\u306e p \u5024\u3001<\/a>\u304a\u3088\u3073\u30e2\u30c7\u30eb\u304c\u30e2\u30c7\u30eb\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u5f79\u7acb\u3064\u305d\u306e\u4ed6\u306e\u6709\u7528\u306a\u5c3a\u5ea6\u306f\u307e\u3060\u308f\u304b\u308a\u307e\u305b\u3093\u3002\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8.\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3002<\/span><\/p>\n
\u6b21\u306b\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3068\u30e2\u30c7\u30eb\u306eR \u4e8c\u4e57\u5024<\/a>\u3092\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n