root_mean_squared_log_error

skfda.misc.scoring.root_mean_squared_log_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')

skfda.misc.scoring.root_mean_squared_log_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')

skfda.misc.scoring.root_mean_squared_log_error(y_true, y_pred, *, sample_weight=None, multioutput='uniform_average')

skfda.misc.scoring.root_mean_squared_log_error(y_true: DataType, y_pred: DataType, *, sample_weight: NDArrayFloat | None = None, multioutput: Literal['uniform_average'] = 'uniform_average') → float

skfda.misc.scoring.root_mean_squared_log_error(y_true: DataType, y_pred: DataType, *, sample_weight: NDArrayFloat | None = None, multioutput: Literal['raw_values']) → DataType

Root Mean Squared Log Error for FData.

This function applies the same logic as mean_squared_log_error, but directly takes the square root of the result. The values of test y_true = [3, 5, 2.5, 7], y_pred = [2.5, 5, 4, 8] come from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_log_error.html

Parameters:

• y_true – True target values.

• y_pred – Predicted values.

• sample_weight – Sample weights.

• multioutput – Return format (raw values or uniform average).

Returns:

Root mean squared logarithmic error.