# Recursive feature elimination with cross-validation¶

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

Out:

Optimal number of features : 3
/home/circleci/project/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()


Total running time of the script: ( 0 minutes 1.982 seconds)

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