In [10]:

    

import numpy as np

from sklearn.datasets import make_classification
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


    

In [5]:

    

X, y = make_classification(n_samples=10000, n_features=10)


    

In [6]:

    

kf = KFold(n_splits=5,random_state=42,shuffle=True)


    

In [12]:

    

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 = accuracy_score(target_test,preds)
    
    print('accuracy: {:.2f}'.format(accuracy))
    
    accuracies.append(accuracy)

average_accuracy = np.mean(accuracies)
print('average accuracy (over all folds): {:.2f}'.format(average_accuracy))


    

    




accuracy: 0.92
accuracy: 0.92
accuracy: 0.91
accuracy: 0.92
accuracy: 0.91
average accuracy (over all folds): 0.92