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MSE

Metric
DashAI.back.metrics.regression.MSE

Average of squared differences between predicted and true values.

Mean Squared Error (MSE) squares each residual before averaging, which penalises large errors much more heavily than small ones. This makes MSE sensitive to outliers: a single large prediction error can dominate the score. It is the most widely used regression loss and is the basis for ordinary least-squares regression and RMSE.

::

MSE(y, ŷ) = (1/N) · Σᵢ (yᵢ - ŷᵢ)²

Range: [0, +∞), lower is better (MAXIMIZE = False). Units are the square of the target variable's unit (use RMSE for the original unit).

References​

Methods​

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

Defined on MSE

Calculate the MSE 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
MSE 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​