mse

improvelib.metrics.mse(y_true, y_pred)

Compute Mean Squared Error (MSE). Uses sklearn.metrics.mean_square_error().

Used in compute_metrics and can be used directly.

Parameters:

y_truenp.ndarray

Ground truth.

y_prednp.ndarray

Predictions made by the model.

Returns:

msefloat

The computed MSE.

Example

This function is called by compute_metrics which is called by compute_performance_scores, which also saves the scores.

You can use this function directly, for example during model training like so:

import improvelib.metrics as metr
mse_to_monitor = metr.mse(y_true, y_pred)