Loss Functions: An Explainer - KDnuggets

Loss function is a method that evaluates how well the algorithm learns the data and produces correct outputs. It computes the distance between our predicted value and the actual value using a mathematical formula. In layman's terms, a loss function measures how wrong the model is in terms of its ability to estimate the relationship between x and y. Below is a list of types of loss functions for both Classification and Regression tasks. Cross Entropy and Log Loss measure the same thing, however they are not the same and is used for Classification tasks.