import numpy as np
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn import datasets
from sklearn import svm

iris = datasets.load_iris()
# Split the iris data into train/test data sets with 40% reserved for testing
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)

# Build an SVC model for predicting iris classifications using training data
clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)

# Now measure its performance with the test data
clf.score(X_test, y_test)
0.96666666666666667
# We give cross_val_score a model, the entire data set and its "real" values, and the number of folds:
scores = cross_val_score(clf, iris.data, iris.target, cv=5)

# Print the accuracy for each fold:
print(scores)

# And the mean accuracy of all 5 folds:
print(scores.mean())
[ 0.96666667  1.          0.96666667  0.96666667  1.        ]
0.98
clf = svm.SVC(kernel='poly', C=1).fit(X_train, y_train)
scores = cross_val_score(clf, iris.data, iris.target, cv=5)
print(scores)
print(scores.mean())
[ 1.          1.          0.9         0.93333333  1.        ]
0.966666666667
# Build an SVC model for predicting iris classifications using training data
clf = svm.SVC(kernel='poly', C=1).fit(X_train, y_train)

# Now measure its performance with the test data
clf.score(X_test, y_test)
0.96666666666666667