5. Evaluation using Cross-Validation

• Use the cross validation feature to splits the training set into 10 distinct folds, then trains and evaluates the Decision Tree model 10 times, picking a different fold for evaluation every time.

def display_scores(scores):

    print("Scores:", scores)

    print("Mean:", scores.mean())

    print("Standard deviation:", scores.std())

from sklearn.model_selection import cross_val_score

scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)

tree_rmse_scores = np.sqrt(-scores)

display_scores(tree_rmse_scores)

Scores: [70274.7991723 67258.3624668 71350.42593227 68882.91340979 70987.99296566 74177.52037059 70788.57311306 70850.53018019 76430.62239321 70212.6471067 ]

Mean: 71121.4387110585

Standard deviation: 2434.3080046605132

• Compute the same scores for the Linear Regression model

lin_scores = cross_val_score(lin_reg, housing_prepared, housing_labels, scoring="neg_mean_squared_error", cv=10)

lin_rmse_scores = np.sqrt(-lin_scores)

display_scores(lin_rmse_scores)

Scores: [66877.52325028 66608.120256 70575.91118868 74179.94799352 67683.32205678 71103.16843468 64782.65896552 67711.29940352 71080.40484136 67687.6384546 ]

Mean: 68828.99948449328

Standard deviation: 2662.761570610345