Sequential Forward Selection

Sequential_Forward_Selection.py

from mlxtend.feature_selection import SequentialFeatureSelector
sfs = SequentialFeatureSelector(LinearRegression(),         # cv = k-fold cross validation, floating is another extension of SFS, not used here
           k_features=10, 
           forward=True, 
           floating=False,
           scoring='accuracy',
           cv=2)   
sfs = sfs.fit(X_train, y_train)

selected_features = x_train.columns[list(sfs.k_feature_idx_)]
print(selected_features)

# print the selected features.
selected_features = x_train.columns[list(sfs.k_feature_idx_)]
print(selected_features)

# final prediction score.
print(sfs.k_score_)

# transform to the newly selected features.
x_train_new = sfs.transform(X_train)