Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.# Import the necessary modules
from sklearn.model_selection import KFold, cross_val_score
# Create a KFold object
kf = KFold(n_splits=6, shuffle=True, random_state=5)
reg = LinearRegression()
# Compute 6-fold cross-validation scores
cv_scores = cross_val_score(reg, X, y, cv=kf)
# Print scores
print(cv_scores)
# Print the mean
print(np.mean(cv_results))
# Print the standard deviation
print(np.std(cv_results))
# Print the 95% confidence interval
print(np.quantile(cv_results, [0.025, 0.975]))>>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Accuracy: 0.98 (+/- 0.03)>>> from sklearn.model_selection import cross_val_score
>>> clf = svm.SVC(kernel='linear', C=1)
>>> scores = cross_val_score(clf, iris.data, iris.target, cv=5)
>>> scores
array([ 0.96..., 1. ..., 0.96..., 0.96..., 1. ])>>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Accuracy: 0.98 (+/- 0.03)>>> print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
Accuracy: 0.98 (+/- 0.03)