import pandas as pd from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression from sklearn.datasets import load_iris data = load_iris() X, y = data.data, data.target # Create a logistic regression model (you can use any other estimator as well). estimator = LogisticRegression() # Set the number of features you want to select (you can adjust this value as needed). num_features_to_select = 2 # Perform RFE to get the best 'num_features_to_select' features. rfe = RFE(estimator, n_features_to_select=num_features_to_select) X_rfe = rfe.fit_transform(X, y) # Get the selected feature indices. selected_feature_indices = rfe.support_ # Get the selected feature names. selected_feature_names = [feature_name for feature_name, selected in zip(data.feature_names, selected_feature_indices) if selected]