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# aswalin/Linear_Regression_Python Last active Jul 1, 2018

Understanding the difference between R_squared and Adjusted R_squared
 import numpy as np import pandas as pd from sklearn import datasets, linear_model def metrics(m,X,y): yhat = m.predict(X) print(yhat) SS_Residual = sum((y-yhat)**2) SS_Total = sum((y-np.mean(y))**2) r_squared = 1 - (float(SS_Residual))/SS_Total adj_r_squared = 1 - (1-r_squared)*(len(y)-1)/(len(y)-X.shape[1]-1) return r_squared,adj_r_squared data = pd.DataFrame({"x1": [1,2,3,4,5], "x2": [2.1,4,6.1,8,10.1]}) y = np.array([2.1, 4, 6.2, 8, 9]) model1 = linear_model.LinearRegression() model1.fit( data.drop("x2", axis = 1),y) metrics(model1,data.drop("x2", axis=1),y) model2 = linear_model.LinearRegression() model2.fit( data,y) metrics(model2,data,y) data = pd.DataFrame({"x1": [1,2,3,4,5], "x2": [2.1,4,6.1,8,10.1]} ) y = np.array([2.1, 4, 6.2, 8, 9]) model3 = linear_model.LinearRegression() model3.fit( data,y) metrics(model3,data,y)