006savariables.py

# Fit and predict
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

# Evaluate
print('Accuracy score:', accuracy_score(y_test, y_pred))
print("-"*80)
print('Confusion matrix\n')
conmat = np.array(confusion_matrix(y_test, y_pred, labels=[1,0]))
confusion = pd.DataFrame(conmat, index=['Actual +', 'Actual -'],
                         columns=['Predicted +','Predicted -'])
print(confusion)
print("-"*80)
print('Classification report')
print(classification_report(y_test, y_pred, target_names=['+','-']))