## How to use Regression Metrics in Python
## DataSet: skleran.load_boston()
def Snippet_182(): 
    print()
    print(format('How to use Regression Metrics in Python','*^82'))    
    
    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from sklearn import datasets
    from sklearn import tree, model_selection
    from sklearn.model_selection import train_test_split
    import matplotlib.pyplot as plt    
    
    plt.style.use('ggplot')

    # load the iris datasets
    seed = 42
    dataset = datasets.load_boston()
    X = dataset.data; y = dataset.target
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
    kfold = model_selection.KFold(n_splits=10, random_state=seed)    
    
    # fit a tree.DecisionTreeClassifier() model to the data
    model = tree.DecisionTreeRegressor()

    # metrics -> Mean Absolute Error
    scoring = 'neg_mean_absolute_error'
    results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
    print(); print("Mean Absolute Error: ", results.mean()); print("Standard Deviation: ", results.std()) 

    # metrics -> Mean Squred Error
    scoring = 'neg_mean_squared_error'
    results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
    print(); print("Mean Squared Error: ", results.mean()); print("Standard Deviation: ", results.std()) 

    # metrics -> R squared
    scoring = 'r2'
    results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
    print(); print("R squared val: ", results.mean()); print("Standard Deviation: ", results.std()) 

Snippet_182()

*********************How to use Regression Metrics in Python**********************

Mean Absolute Error:  -2.9191465149359885
Standard Deviation:  0.6656357537118691

Mean Squared Error:  -23.698743954480797
Standard Deviation:  17.884586385352065

R squared val:  0.5879644385537106
Standard Deviation:  0.2689916551008991