In optimization, a loss function is a metric that assesses the performance of a model by quantifying the difference between the model's predicted output and the true or expected output (see theory).

The exact form of the loss function depends on the nature of the problem to be solved, the data available, and the type of optimization algorithm being used. Therefore, finding appropriate loss functions is one of the most crucial research endeavors in inverse problems [1].

This section presents some loss equations for regression tasks.

Reference list

ID	Reference
[1]	Lorenzo Ciampiconi, Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza (2023). A survey and taxonomy of loss functions in machine learning.

---

Table of contents

• loss_function_mse
• loss_function_mae
• loss_function_mape
• loss_function_hubber
• loss_function_rmse
• loss_function_r2
• loss_function_r2_adjusted