{"id":3009,"date":"2023-07-19T15:56:18","date_gmt":"2023-07-19T15:56:18","guid":{"rendered":"https:\/\/statorials.org\/ar\/sklearn-%d9%85%d9%84%d8%ae%d8%b5-%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a\/"},"modified":"2023-07-19T15:56:18","modified_gmt":"2023-07-19T15:56:18","slug":"sklearn-%d9%85%d9%84%d8%ae%d8%b5-%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/sklearn-%d9%85%d9%84%d8%ae%d8%b5-%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0644\u062e\u0635 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0646 scikit-learn"},"content":{"rendered":"

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\u0641\u064a \u0643\u062b\u064a\u0631 \u0645\u0646 \u0627\u0644\u0623\u062d\u064a\u0627\u0646 \u0642\u062f \u062a\u0631\u063a\u0628 \u0641\u064a \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u0644\u062e\u0635 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0630\u064a \u062a\u0645 \u0625\u0646\u0634\u0627\u0624\u0647 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 scikit-Learn<\/a> \u0641\u064a Python.<\/span><\/p>\n

\u0644\u0633\u0648\u0621 \u0627\u0644\u062d\u0638\u060c \u0644\u0627 \u064a\u0642\u062f\u0645 scikit-learn \u0627\u0644\u0639\u062f\u064a\u062f \u0645\u0646 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u0645\u0636\u0645\u0646\u0629 \u0644\u062a\u062d\u0644\u064a\u0644 \u0645\u0644\u062e\u0635 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631\u060c \u062d\u064a\u062b \u064a\u062a\u0645 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647 \u0639\u0645\u0648\u0645\u064b\u0627 \u0644\u0644\u0623\u063a\u0631\u0627\u0636 \u0627\u0644\u062a\u0646\u0628\u0624\u064a\u0629<\/a> \u0641\u0642\u0637.<\/span><\/p>\n

\u0644\u0630\u0627\u060c \u0625\u0630\u0627 \u0643\u0646\u062a \u062a\u0631\u063a\u0628 \u0641\u064a \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0644\u062e\u0635 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u0641\u0644\u062f\u064a\u0643 \u062e\u064a\u0627\u0631\u0627\u0646:<\/span><\/p>\n

1.<\/strong> \u0627\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0627\u0644\u0645\u062d\u062f\u0648\u062f\u0629 \u0644\u0628\u0631\u0646\u0627\u0645\u062c scikit-Learn.<\/span><\/p>\n

2.<\/strong> \u0627\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a\u0629<\/a> \u0628\u062f\u0644\u0627\u064b \u0645\u0646 \u0630\u0644\u0643.<\/span><\/p>\n

\u062a\u0648\u0636\u062d \u0627\u0644\u0623\u0645\u062b\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0643\u0644 \u0637\u0631\u064a\u0642\u0629 \u0639\u0645\u0644\u064a\u064b\u0627 \u0645\u0639 \u0627\u0644\u0628\u0627\u0646\u062f\u0627 DataFrame \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/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
 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u0623\u0648\u0644\u0649: \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0644\u062e\u0635 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0646 Scikit-Learn<\/strong><\/span><\/h3>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u064a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/a> \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 scikit-learn:<\/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
 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0627 \u0627\u0644\u0646\u0627\u062a\u062c \u064a\u0645\u0643\u0646\u0646\u0627 \u0643\u062a\u0627\u0628\u0629 \u0645\u0639\u0627\u062f\u0644\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0644\u0627\u0626\u0645:<\/span><\/p>\n
 \u0635 = 70.48 + 5.79×1<\/sub> – 1.16×2<\/sub><\/span><\/p>\n
 \u0648\u064a\u0645\u0643\u0646 \u0623\u064a\u0636\u064b\u0627 \u0645\u0644\u0627\u062d\u0638\u0629 \u0623\u0646 \u0642\u064a\u0645\u0629 R2<\/sup> \u0644\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u064a 76.67.<\/span><\/p>\n
 \u0648\u0647\u0630\u0627 \u064a\u0639\u0646\u064a \u0623\u0646 76.67%<\/strong> \u0645\u0646 \u0627\u0644\u062a\u0628\u0627\u064a\u0646 \u0641\u064a \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u064a\u0645\u0643\u0646 \u062a\u0641\u0633\u064a\u0631\u0647 \u0645\u0646 \u062e\u0644\u0627\u0644 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u064a\u0646 \u0627\u0644\u0645\u062a\u0646\u0628\u0626\u064a\u0646 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c.<\/span><\/p>\n
 \u0627\u0644\u0637\u0631\u064a\u0642\u0629 \u0627\u0644\u062b\u0627\u0646\u064a\u0629: \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0645\u0644\u062e\u0635 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0646 Statsmodels<\/strong><\/span><\/h3>\n
 \u0625\u0630\u0627 \u0643\u0646\u062a \u062a\u0631\u063a\u0628 \u0641\u064a \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u0644\u062e\u0635 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u0641\u0645\u0646 \u0627\u0644\u0623\u0641\u0636\u0644 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062d\u0632\u0645\u0629 statsmodels<\/strong> .<\/span><\/p>\n
 \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u062d\u0632\u0645\u0629 \u0644\u062a\u0646\u0627\u0633\u0628 \u0646\u0641\u0633 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f \u0645\u062b\u0644 \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u0633\u0627\u0628\u0642 \u0648\u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u0644\u062e\u0635 \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/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
 \u0644\u0627\u062d\u0638 \u0623\u0646 \u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0648\u0642\u064a\u0645\u0629 R-squared \u062a\u062a\u0637\u0627\u0628\u0642 \u0645\u0639 \u062a\u0644\u0643 \u0627\u0644\u0645\u062d\u0633\u0648\u0628\u0629 \u0628\u0648\u0627\u0633\u0637\u0629 scikit-learn\u060c \u0648\u0644\u0643\u0646 \u0644\u062f\u064a\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0644\u0643\u062b\u064a\u0631 \u0645\u0646 \u0627\u0644\u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u0645\u0641\u064a\u062f\u0629 \u0627\u0644\u0623\u062e\u0631\u0649 \u0644\u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631.<\/span><\/p>\n
 \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0631\u0624\u064a\u0629 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0644\u0643\u0644 \u0645\u062a\u063a\u064a\u0631 \u0645\u062a\u0646\u0628\u0626 \u0641\u0631\u062f\u064a:<\/span><\/p>\n
 \u0639\u0644\u0649 \u0627\u0644\u0631\u063a\u0645 \u0645\u0646 \u0623\u0646 \u0647\u0630\u0647 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u0645\u0641\u064a\u062f\u0629\u060c \u0625\u0644\u0627 \u0623\u0646\u0646\u0627 \u0645\u0627 \u0632\u0644\u0646\u0627 \u0644\u0627 \u0646\u0639\u0631\u0641 \u0625\u062d\u0635\u0627\u0626\u064a\u0629 F \u0627\u0644\u0625\u062c\u0645\u0627\u0644\u064a\u0629<\/a> \u0644\u0644\u0646\u0645\u0648\u0630\u062c\u060c \u0648\u0627\u0644\u0642\u064a\u0645 p \u0644\u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0641\u0631\u062f\u064a\u0629\u060c<\/a> \u0648\u063a\u064a\u0631\u0647\u0627 \u0645\u0646 \u0627\u0644\u062a\u062f\u0627\u0628\u064a\u0631 \u0627\u0644\u0645\u0641\u064a\u062f\u0629 \u0627\u0644\u062a\u064a \u064a\u0645\u0643\u0646 \u0623\u0646 \u062a\u0633\u0627\u0639\u062f\u0646\u0627 \u0641\u064a \u0641\u0647\u0645 \u0645\u062f\u0649 \u0645\u0644\u0627\u0621\u0645\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0644\u0646\u0645\u0648\u0630\u062c. dataset.dataset.<\/span><\/p>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0628\u0639\u062f \u0630\u0644\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u0627\u0633\u062a\u062e\u0631\u0627\u062c \u0645\u0639\u0627\u0645\u0644\u0627\u062a \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0645\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0628\u0627\u0644\u0625\u0636\u0627\u0641\u0629 \u0625\u0644\u0649 \u0642\u064a\u0645\u0629 R-squared<\/a> \u0644\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/span><\/p>\n