Recursive feature elimination with cross-validation

A recursive feature elimination example with automatic tuning of the number of features selected with cross-validation.

Optimal number of features : 3
/home/runner/work/scikit-learn/scikit-learn/sklearn/utils/deprecation.py:103: FutureWarning: The `grid_scores_` attribute is deprecated in version 1.0 in favor of `cv_results_` and will be removed in version 1.2.
  warnings.warn(msg, category=FutureWarning)

import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import RFECV
from sklearn.datasets import make_classification

# Build a classification task using 3 informative features
X, y = make_classification(
    n_samples=1000,
    n_features=25,
    n_informative=3,
    n_redundant=2,
    n_repeated=0,
    n_classes=8,
    n_clusters_per_class=1,
    random_state=0,
)

# Create the RFE object and compute a cross-validated score.
svc = SVC(kernel="linear")
# The "accuracy" scoring shows the proportion of correct classifications

min_features_to_select = 1  # Minimum number of features to consider
rfecv = RFECV(
    estimator=svc,
    step=1,
    cv=StratifiedKFold(2),
    scoring="accuracy",
    min_features_to_select=min_features_to_select,
)
rfecv.fit(X, y)

print("Optimal number of features : %d" % rfecv.n_features_)

# Plot number of features VS. cross-validation scores
plt.figure()
plt.xlabel("Number of features selected")
plt.ylabel("Cross validation score (accuracy)")
plt.plot(
    range(min_features_to_select, len(rfecv.grid_scores_) + min_features_to_select),
    rfecv.grid_scores_,
)
plt.show()