Regression with RFE

RFE_Regression.py

from sklearn.feature_selection import RFE
lm = LinearRegression()
rfe1 = RFE(lm, 20)   # RFE with 20 features

# Fit on train and test data with 20 features
X_train_new = rfe1.fit_transform(X_train, y_train)
X_test_new = rfe1.transform(X_test)

# Print the boolean results
print(rfe1.support_)       # Output [False False False False  True False False False  True False False...]
print(rfe1.ranking_)       # Output [36 34 23 26  1 21 12 27  1 13 28  1 18 19 32 25  1 11  9  7  8 10 30 35...] 

lm.fit(X_train_new, y_train)
predictions_rfe = lm.predict(X_test_new)
RMSE = np.sqrt(mean_squared_error(y_test, predictions_rfe))
R2 = r2_score(y_test, predictions)
print('R2:',R2,'RMSE:',RMSE)     #Output R2: 0.88 RMSE: 0.33