• 注意多維數組 MAE 的計算方法 *

>>> from sklearn.metrics import mean_absolute_error >>> y_true = [3, -0.5, 2, 7] >>> y_pred = [2.5, 0.0, 2, 8] >>> mean_absolute_error(y_true, y_pred) 0.5 >>> y_true = [[0.5, 1], [-1, 1], [7, -6]] >>> y_pred = [[0, 2], [-1, 2], [8, -5]] >>> mean_absolute_error(y_true, y_pred) 0.75 >>> mean_absolute_error(y_true, y_pred, multioutput='raw_values') array([0.5, 1. ]) >>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]) ... 0.85... In [34]: y_true = np.array([1,2,3,4,5,0,0,0,0,0]) In [35]: y_pred = np.array([1.1,2.2,3.1,4.1,5.1,0,0,0,0,0]) In [36]: mean_absolute_error(y_true,y_pred) Out[36]: 0.05999999999999996In [37]: y_pred = np.array([1.1,2.2,3.1,4.1,5.1]) In [38]: y_true = np.array([1,2,3,4,5]) In [39]: mean_absolute_error(y_true,y_pred) Out[39]: 0.11999999999999993

• multioutput='raw_values' 給出的是每列的 MAE
• multioutput=[0.3, 0.7] 給出的是加了不同權重的每列的MAE

轉載于:https://www.cnblogs.com/yaos/p/9878497.html

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