A loss function is a mathematical function that measures the difference between a model's prediction and the correct target value. During training, model parameters are adjusted to minimize this loss. Different tasks require different loss functions, such as mean squared error for regression or log loss for probabilistic classification.
During training or testing, a mathematical function that calculates the loss on a batch of examples. A loss function returns a lower loss for models that makes good predictions than for models that make bad predictions. The goal of training is typically to minimize the loss that a loss function returns. Many different kinds of loss functions exist. Pick the appropriate loss function for the kind of model you are building. For example: - L~2~ loss (or Mean Squared Error) is the loss function for linear regression. - Log Loss is the loss function for logistic regression.
A loss function is the difference between the training label values and the prediction made by the model. The parameters of the model are estimated by minimizing the loss function.