Overview
Multi-class loss functions are vital in machine learning for evaluating models that predict multiple categories. They help in optimizing the model's performance by quantifying prediction errors, guiding the training process. The most common loss function for multi-class classification is cross-entro...
Key Terms
Example: Classifying images of animals into categories like cats, dogs, and birds.
Example: Mean Squared Error is a common loss function for regression tasks.
Example: Used in multi-class classification to compare predicted probabilities with actual classes.
Example: Used in the output layer of a neural network for multi-class classification.
Example: Encoding the classes 'cat', 'dog', and 'bird' as [1,0,0], [0,1,0], and [0,0,1].
Example: Logits can be any real number, which are then transformed into probabilities.