RMSE

Metric

DashAI.back.metrics.regression.RMSE

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Square root of the mean squared error between predicted and true values.

Root Mean Squared Error (RMSE) is the square root of MSE, bringing the metric back to the same unit as the target variable. Like MSE, it penalises large errors more heavily than MAE, but its interpretability advantage over MSE makes it the standard regression error metric in most applications. RMSE is equivalent to the Euclidean distance between the prediction vector and the true value vector, normalised by sample count.

::

RMSE(y, ŷ) = sqrt( (1/N) · Σᵢ (yᵢ - ŷᵢ)² )

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

References

• [1] https://scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html

Methods

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

Defined on RMSE

Calculate the RMSE 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

RMSE 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

• RegressionTask