smooth.ES.loss_

property ES.loss_: str

$loss).

The objective function minimized during model fitting. Uses trailing underscore to distinguish from the input parameter and follow scikit-learn convention for fitted attributes.

Returns:

Loss function name, one of: - "likelihood": Maximum likelihood (default) - "MSE": Mean Squared Error - "MAE": Mean Absolute Error - "HAM": Half Absolute Moment - "LASSO": L1 regularization - "RIDGE": L2 regularization - "GTMSE": Geometric Trace MSE - "GPL": Generalized Predictive Likelihood

Return type:

str

Raises:

ValueError – If the model has not been fitted yet.

See also

loss_value

The optimized loss function value

distribution_

Error distribution used

Examples

>>> model = ADAM(model="AAN", loss="MAE")
>>> model.fit(y)
>>> print(model.loss_)
'MAE'

Type:

Loss function used for parameter estimation (R