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from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

from sklearn.model_selection import cross_val_score

clf = RandomForestClassifier(max_depth=10, random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print('Without Scaling and without CV: ', accuracy_score(y_test, y_pred))
scores = cross_val_score(clf, X_train, y_train, cv=10)
print('Without Scaling and With CV: ', scores.mean())