讓我們來定義一下y_true = np.array([3, -0.5, 2, 7])

y_pred = np.array([2.5, 0.0, 2, 8])

平均絕對誤差可定義為

^{pr2}$

絕對誤差方差為np.var(np.abs(y_true - y_pred)) # 0.125

誤差方差為np.var((y_true - y_pred)) # 0.3125

現在如何用scikit-learn實現它？在from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

# X and target data and train test split

boston = datasets.load_boston()

X, y = boston.data, boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

# initialize and fit to your train data and predict on test data

clf = LinearRegression()

clf.fit(X_train, y_train)

preds = clf.predict(X_test)

# evaluate

mean_absolute_error(y_test, preds) == np.mean(np.abs(y_test - preds))

# get the variance of (absolute) residuals

np.var(np.abs(y_test - preds))

np.var((y_test - preds))

總結

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