Mastering Python for Finance - Second Edition

Loss functions

The loss function computes the error between the predicted value of a model and the actual value. The smaller the error value, the better the model is in prediction. Some loss functions used in regression-based models are:

• Mean squared error (MSE) loss
• Mean absolute error (MAE) loss
• Huber loss
• Quantile loss

Some loss functions used in classification-based models are:

• Focal loss
• Hinge loss
• Logistic loss
• Exponential loss