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from sklearn.datasets import samples_generator from sklearn.ensemble import RandomForestClassifier from sklearn.feature_selection import SelectKBest, f_regression from sklearn.pipeline import Pipeline # generate sample data X, y = samples_generator.make_classification( n_informative=4, n_features=20, n_redundant=0, random_state=5) # Feature selector selector_k_best = SelectKBest(f_regression, k=10) # Random forest classifier classifier = RandomForestClassifier(n_estimators=50, max_depth=4) # Build the machine learning pipeline pipeline_classifier = Pipeline([('selector', selector_k_best), ('rf', classifier)]) # We can set the parameters using the names we assigned # earlier. For example, if we want to set 'k' to 6 in the # feature selector and set 'n_estimators' in the Random # Forest Classifier to 25, we can do it as shown below pipeline_classifier.set_params(selector__k=6, rf__n_estimators=25) # Training the classifier pipeline_classifier.fit(X, y) # Predict the output prediction = pipeline_classifier.predict(X) print("\nPredictions:\n", prediction) # Print score print("\nScore:", pipeline_classifier.score(X, y)) # Print the selected features chosen by the selector features_status = pipeline_classifier.named_steps['selector'].get_support() selected_features = [] for count, item in enumerate(features_status): if item: selected_features.append(count) print("\nSelected features (0-indexed):", ', '.join([str(x) for x in selected_features])) |
