Recursive feature elimination with cross-validation

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

plot rfe with cross validation


Optimal number of features : 3
/home/circleci/project/sklearn/utils/ 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),
              min_features_to_select=min_features_to_select), y)

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

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

Total running time of the script: ( 0 minutes 2.380 seconds)

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