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MAE

Metric
DashAI.back.metrics.regression.MAE

Average of absolute differences between predicted and true values.

Mean Absolute Error (MAE) is the simplest regression error metric: it computes the mean of the absolute residuals. Unlike MSE or RMSE, MAE treats all errors equally regardless of magnitude, making it more robust to outliers. Its value is expressed in the same unit as the target variable, which aids interpretability.

::

MAE(y, ŷ) = (1/N) · Σᵢ |yᵢ - ŷᵢ|

Range: [0, +∞), lower is better (MAXIMIZE = False).

References​

Methods​

score(true_values: 'DashAIDataset', pred_values: 'np.ndarray') -> float

Defined on MAE

Calculate the MAE between true values and predicted values.

Parameters

true_values : DashAIDataset
A DashAI dataset with true values.
predicted_values : np.ndarray
A one-dimensional array with the predicted values for each instance.

Returns

float
MAE score between true values and predicted values

get_metadata(cls: 'BaseMetric') -> Dict[str, Any]

Defined on BaseMetric

Get metadata values for the current metric.

Returns

Dict[str, Any]
Dictionary with the metadata

Compatible with​