The easiest way for getting feature names after running SelectKBest in Scikit Learn

Solution 1:

This doesn't require loops.

# Create and fit selector
selector = SelectKBest(f_classif, k=5)
selector.fit(features_df, target)
# Get columns to keep and create new dataframe with those only
cols = selector.get_support(indices=True)
features_df_new = features_df.iloc[:,cols]

Solution 2:

For me this code works fine and is more 'pythonic':

mask = select_k_best_classifier.get_support()
new_features = features_dataframe.columns[mask]

Solution 3:

You can do the following :

mask = select_k_best_classifier.get_support() #list of booleans
new_features = [] # The list of your K best features

for bool, feature in zip(mask, feature_names):
    if bool:
        new_features.append(feature)

Then change the name of your features:

dataframe = pd.DataFrame(fit_transofrmed_features, columns=new_features)

Solution 4:

Following code will help you in finding top K features with their F-scores. Let, X is the pandas dataframe, whose columns are all the features and y is the list of class labels.

import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif
#Suppose, we select 5 features with top 5 Fisher scores
selector = SelectKBest(f_classif, k = 5)
#New dataframe with the selected features for later use in the classifier. fit() method works too, if you want only the feature names and their corresponding scores
X_new = selector.fit_transform(X, y)
names = X.columns.values[selector.get_support()]
scores = selector.scores_[selector.get_support()]
names_scores = list(zip(names, scores))
ns_df = pd.DataFrame(data = names_scores, columns=['Feat_names', 'F_Scores'])
#Sort the dataframe for better visualization
ns_df_sorted = ns_df.sort_values(['F_Scores', 'Feat_names'], ascending = [False, True])
print(ns_df_sorted)