Loss Function: Definition and Examples

The loss function is a fundamental pillar of machine learning. Its role is simple to understand: it assigns a numerical score to each prediction of the model, indicating how far that prediction deviates from reality. The higher this score, the more the model is wrong. The goal of training is therefore to minimize this value.

Concretely, during the training of a neural network, each batch of data passes through the model which produces a prediction. The loss function compares this prediction to the actual value (the label) and calculates an error. This error is then backpropagated through the network to adjust the weights of the neurons via an optimizer like SGD or Adam. This cycle repeats millions of times until the loss converges to a minimum.

There are many loss functions suited to different tasks. For classification, cross-entropy loss is typically used, which heavily penalizes confident but incorrect predictions. For regression, mean squared error (MSE) or mean absolute error (MAE) are common. More specialized tasks like text generation or object detection use custom loss functions.

The choice of loss function directly influences the behavior of the model. A poorly chosen function can lead to a model that optimizes the wrong objective: for example, using MSE for an imbalanced classification problem will produce a mediocre model. In prompt engineering, understanding the loss function helps to better anticipate the biases and behaviors of language models, because it is this function that shaped their responses during training.