{"id":3008,"date":"2023-07-19T15:56:18","date_gmt":"2023-07-19T15:56:18","guid":{"rendered":"https:\/\/statorials.org\/ko\/sklearn-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8\/"},"modified":"2023-07-19T15:56:18","modified_gmt":"2023-07-19T15:56:18","slug":"sklearn-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/sklearn-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8\/","title":{"rendered":"Scikit-learn\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378 \uc694\uc57d\uc744 \uc5bb\ub294 \ubc29\ubc95"},"content":{"rendered":"

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Python\uc5d0\uc11c scikit-learn\uc744<\/a> \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\ub41c \ud68c\uadc0 \ubaa8\ub378\uc758 \uc694\uc57d\uc744 \ucd94\ucd9c\ud558\ub824\ub294 \uacbd\uc6b0\uac00 \uc885\uc885 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubd88\ud589\ud558\uac8c\ub3c4 scikit-learn\uc740 \uc77c\ubc18\uc801\uc73c\ub85c \uc608\uce21 \ubaa9\uc801<\/a> \uc73c\ub85c\ub9cc \uc0ac\uc6a9\ub418\ubbc0\ub85c \ud68c\uadc0 \ubaa8\ub378\uc758 \uc694\uc57d\uc744 \ubd84\uc11d\ud558\uae30 \uc704\ud55c \ub0b4\uc7a5 \uae30\ub2a5\uc744 \ub9ce\uc774 \uc81c\uacf5\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub530\ub77c\uc11c Python\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378 \uc694\uc57d\uc744 \uc5bb\uc73c\ub824\uba74 \ub2e4\uc74c \ub450 \uac00\uc9c0 \uc635\uc158\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

1.<\/strong> scikit-learn\uc758 \uc81c\ud55c\ub41c \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uc138\uc694.<\/span><\/p>\n

2.<\/strong> \ub300\uc2e0 \ud1b5\uacc4 \ubaa8\ub378\uc744<\/a> \uc0ac\uc6a9\ud558\uc2ed\uc2dc\uc624.<\/span><\/p>\n

\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c pandas DataFrame\uc5d0\uc11c \uc2e4\uc81c\ub85c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \ubc29\ubc95 1: Scikit-Learn\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378 \uc694\uc57d \uac00\uc838\uc624\uae30<\/strong><\/span><\/h3>\n
 scikit-learn\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0<\/a> \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uba74 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \uc774 \ucd9c\ub825\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ub300\ud55c \ubc29\uc815\uc2dd\uc744 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 y = 70.48 + 5.79x 1<\/sub> \u2013 1.16x 2<\/sub><\/span><\/p>\n
 \ub610\ud55c \ubaa8\ub378\uc758 R 2<\/sup> \uac12\uc774 76.67\uc784\uc744 \uc54c \uc218 \uc788\ub2e4.<\/span><\/p>\n
 \uc774\ub294 \ubc18\uc751 \ubcc0\uc218\uc758 \ubcc0\ub3d9 \uc911 76.67%\uac00<\/strong> \ubaa8\ub378\uc758 \ub450 \uc608\uce21 \ubcc0\uc218\uc5d0 \uc758\ud574 \uc124\uba85\ub420 \uc218 \uc788\uc74c\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ubc29\ubc95 2: Statsmodels\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378 \uc694\uc57d \uac00\uc838\uc624\uae30<\/strong><\/span><\/h3>\n
 Python\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc758 \uc694\uc57d\uc744 \ucd94\ucd9c\ud558\ub824\uba74 statsmodels<\/strong> \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \uac00\uc7a5 \uc88b\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \uc774 \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774\uc804 \uc608\uc81c\uc640 \ub3d9\uc77c\ud55c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uace0 \ubaa8\ub378 \uc694\uc57d\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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
 \ud68c\uadc0 \uacc4\uc218\uc640 R-\uc81c\uacf1 \uac12\uc740 scikit-learn\uc5d0\uc11c \uacc4\uc0b0\ud55c \uac12\uacfc \uc77c\uce58\ud558\uc9c0\ub9cc \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ub300\ud55c \ub2e4\ub978 \uc720\uc6a9\ud55c \uce21\uc815\ud56d\ubaa9\ub3c4 \ub9ce\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uac01 \uac1c\ubcc4 \uc608\uce21 \ubcc0\uc218\uc5d0 \ub300\ud55c p-\uac12\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uacb0\uacfc\ub294 \uc720\uc6a9\ud558\uc9c0\ub9cc \ubaa8\ub378\uc758 \uc804\uccb4 F \ud1b5\uacc4<\/a> , \uac1c\ubcc4 \ud68c\uadc0 \uacc4\uc218<\/a> \uc758 p-\uac12 \ubc0f \ubaa8\ub378\uc774 \ubaa8\ub378\uc5d0 \uc5bc\ub9c8\ub098 \uc798 \ub9de\ub294\uc9c0 \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub420 \uc218 \uc788\ub294 \uae30\ud0c0 \uc720\uc6a9\ud55c \uce21\uc815\uac12\uc744 \uc544\uc9c1 \uc54c\uc9c0 \ubabb\ud569\ub2c8\ub2e4. \ub370\uc774\ud130\uc138\ud2b8.\ub370\uc774\ud130\uc138\ud2b8.<\/span><\/p>\n
 \uadf8\ub7f0 \ub2e4\uc74c \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \ud68c\uadc0 \uacc4\uc218\uc640 \ubaa8\ub378\uc758 R \uc81c\uacf1 \uac12\uc744<\/a> \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n