@@ -1029,19 +1029,18 @@ def macro_averaged_mean_absolute_error(y_true, y_pred):
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>>> import numpy as np
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>>> from sklearn.metrics import mean_absolute_error
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>>> from imblearn.metrics import macro_averaged_mean_absolute_error
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- >>> y_true_balanced = [1, 1, 1, 2, 2, 2]
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- >>> y_true_imbalanced = [1, 1, 1, 1, 1, 2]
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- >>> y_pred = [1, 2, 1, 2, 1, 2]
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- >>> np.round(mean_absolute_error(y_true_balanced, y_pred), 4)
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- 0.3333
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- >>> np.round(mean_absolute_error(y_true_imbalanced, y_pred), 4)
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- 0.3333
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- >>> np.round(macro_averaged_mean_absolute_error(y_true_balanced, y_pred),
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- 4)
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- 0.3333
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+ >>> y_true_balanced = [1, 1, 2, 2]
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+ >>> y_true_imbalanced = [1, 2, 2, 2]
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+ >>> y_pred = [1, 2, 1, 2]
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+ >>> mean_absolute_error(y_true_balanced, y_pred)
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+ 0.5
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+ >>> mean_absolute_error(y_true_imbalanced, y_pred)
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+ 0.25
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+ >>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
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+ 0.5
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>>> np.round(macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred,
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4)
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- 0.2
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+ 0.1667
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"""
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all_mae = []
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