losses

Common loss functions for optimization.

Functions

hinge_loss(y_true, y_pred, operator[, ...])	Applies a differentiable one-sided constraint (hinge) before evaluating a base metric.
huber_loss(y_true, y_pred[, delta, ...])	Computes the Huber loss, a robust loss function that transitions from squared error to absolute error depending on the delta threshold.
log_mean_squared_error(y_true, y_pred[, ...])	Computes the log of the Mean Squared Error (MSE) between true and predicted values.
mean_absolute_error(y_true, y_pred[, ...])	Computes the Mean Absolute Error (MAE) between true and predicted values.
mean_absolute_percentage_error(y_true, y_pred)	Computes the Mean Absolute Percentage Error (MAPE) between true and predicted values.
mean_squared_error(y_true, y_pred[, ...])	Computes the Mean Squared Error (MSE) between true and predicted values.
root_mean_squared_error(y_true, y_pred[, ...])	Computes the Root Mean Squared Error (RMSE) between true and predicted values.