root_mean_squared_error

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

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

skfda.misc.scoring.root_mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray, *, sample_weight: np.ndarray | None = None, multioutput: Literal['uniform_average'] = 'uniform_average') → float

skfda.misc.scoring.root_mean_squared_error(y_true: np.ndarray, y_pred: np.ndarray, *, sample_weight: np.ndarray | None = None, multioutput: Literal['raw_values']) → np.ndarray

skfda.misc.scoring.root_mean_squared_error(y_true: FData, y_pred: FData, *, sample_weight: np.ndarray | None = None, multioutput: Literal['uniform_average'] = 'uniform_average') → float

skfda.misc.scoring.root_mean_squared_error(y_true: FData, y_pred: FData, *, sample_weight: np.ndarray | None = None, multioutput: Literal['raw_values']) → FData

Root Mean Squared Error for FData. With \(y\_true = (X_1, X_2, ..., X_n)\) being the real values, \(t\_pred = (\hat{X}_1, \hat{X}_2, ..., \hat{X}_n)\) being the estimated and \(sample\_weight = (w_1, w_2, ..., w_n)\), the error is calculated as

\[RMSE(y\_true, y\_pred)(t) = \sqrt{\frac{1}{\sum w_i} \sum_{i=1}^n w_i(X_i(t) - \hat{X}_i(t))^2}\]

This is the square root of MSE (Mean Squared Error). The values of test y_true = [3, -0.5, 2, 7], y_pred = [2.5, 0.0, 2, 8] come from https://scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html

Parameters:

• y_true – Correct target values.

• y_pred – Estimated values.

• sample_weight – Sample weights. By default, uniform weights are taken.

• multioutput – Defines format of the return.

Returns:

Root mean squared error.