Cross-Validation Examples with Scikit-LearnLast updated:
Table of Contents
WIP Alert This is a work in progress. Current information is correct but more content may be added in the future.
K-Fold: Manual Splits
from sklearn.model_selection import KFold kf = KFold(n_splits=5,random_state=42,shuffle=True) # these are you training data points: # features and targets X = .... y = .... accuracies =  for train_index, test_index in kf.split(X): data_train = X[train_index] target_train = y[train_index] data_test = X[test_index] target_test = y[test_index] # if needed, do preprocessing here clf = LogisticRegression() clf.fit(data_train,target_train) preds = clf.predict(data_test) # accuracy for the current fold only accuracy = accuracy_score(target_test,preds) accuracies.append(accuracy) # this is the average accuracy over all folds average_accuracy = np.mean(accuracies)