Plot Feature Selection Pipeline¶
================== Pipeline ANOVA SVM¶
This example shows how a feature selection can be easily integrated within a machine learning pipeline.
We also show that you can easily inspect part of the pipeline.
Imports for ANOVA-SVM Feature Selection Pipeline¶
Embedding feature selection inside a Pipeline prevents data leakage and ensures correct evaluation: A common mistake is fitting SelectKBest on the entire dataset (including test data) before splitting, which leaks information about the test set’s feature distributions into the selection process. By placing SelectKBest(f_classif, k=3) as the first step in make_pipeline, the ANOVA F-test statistics are computed exclusively on training data during fit, and the same selected feature indices are applied to transform the test data during predict. This guarantees that the test set accuracy reflects genuine generalization performance.
The pipeline’s inverse_transform traces selected features back to the original feature space: After training, inspecting anova_svm[-1].coef_ shows only 3 coefficients (matching the 3 selected features), but calling anova_svm[:-1].inverse_transform() on those coefficients maps them back to the full 20-dimensional space with zeros in unselected positions. This reveals which of the original 20 features (3 informative, 17 noise from make_classification) were retained by the ANOVA filter, confirming that the F-test correctly identified the most discriminative features for the LinearSVC classifier.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# %%
# We will start by generating a binary classification dataset. Subsequently, we
# will divide the dataset into two subsets.
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
X, y = make_classification(
n_features=20,
n_informative=3,
n_redundant=0,
n_classes=2,
n_clusters_per_class=2,
random_state=42,
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# %%
# A common mistake done with feature selection is to search a subset of
# discriminative features on the full dataset, instead of only using the
# training set. The usage of scikit-learn :func:`~sklearn.pipeline.Pipeline`
# prevents to make such mistake.
#
# Here, we will demonstrate how to build a pipeline where the first step will
# be the feature selection.
#
# When calling `fit` on the training data, a subset of feature will be selected
# and the index of these selected features will be stored. The feature selector
# will subsequently reduce the number of features, and pass this subset to the
# classifier which will be trained.
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC
anova_filter = SelectKBest(f_classif, k=3)
clf = LinearSVC()
anova_svm = make_pipeline(anova_filter, clf)
anova_svm.fit(X_train, y_train)
# %%
# Once the training is complete, we can predict on new unseen samples. In this
# case, the feature selector will only select the most discriminative features
# based on the information stored during training. Then, the data will be
# passed to the classifier which will make the prediction.
#
# Here, we show the final metrics via a classification report.
from sklearn.metrics import classification_report
y_pred = anova_svm.predict(X_test)
print(classification_report(y_test, y_pred))
# %%
# Be aware that you can inspect a step in the pipeline. For instance, we might
# be interested about the parameters of the classifier. Since we selected
# three features, we expect to have three coefficients.
anova_svm[-1].coef_
# %%
# However, we do not know which features were selected from the original
# dataset. We could proceed by several manners. Here, we will invert the
# transformation of these coefficients to get information about the original
# space.
anova_svm[:-1].inverse_transform(anova_svm[-1].coef_)
# %%
# We can see that the features with non-zero coefficients are the selected
# features by the first step.