macro_averaged_mean_absolute_error

imbens.metrics.macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None)[source]

Compute Macro-Averaged Mean Absolute Error (MA-MAE) for imbalanced ordinal classification.

This function computes each MAE for each class and average them, giving an equal weight to each class.

Read more in the User Guide.

Parameters:

y_truearray-like of shape (n_samples,) or (n_samples, n_outputs): Ground truth (correct) target values.
y_predarray-like of shape (n_samples,) or (n_samples, n_outputs): Estimated targets as returned by a classifier.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

lossfloat or ndarray of floats: Macro-Averaged MAE output is non-negative floating point. The best value is 0.0.

Examples

>>> import numpy as np
>>> from sklearn.metrics import mean_absolute_error
>>> from imbens.metrics import macro_averaged_mean_absolute_error
>>> y_true_balanced = [1, 1, 2, 2]
>>> y_true_imbalanced = [1, 2, 2, 2]
>>> y_pred = [1, 2, 1, 2]
>>> mean_absolute_error(y_true_balanced, y_pred)
0.5
>>> mean_absolute_error(y_true_imbalanced, y_pred)
0.25
>>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
0.5
>>> macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred)
0.16666666666666666