## How to generate classification report and confusion matrix in Python
def Snippet_138():
    print()
    print(format('How to generate classification report & confusion matrix in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import classification_report, confusion_matrix

    # Load data
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    # Create list of target class names
    class_names = iris.target_names

    # Create training and test set
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

    # Create DecisionTree model
    classifier = DecisionTreeClassifier()

    # Train model and make predictions
    y_hat = classifier.fit(X_train, y_train).predict(X_test)

    # Create a classification report
    print()
    print(classification_report(y_test, y_hat, target_names=class_names))

    # Create a confusion matrix
    print()
    print(confusion_matrix(y_test, y_hat))

Snippet_138()