Cross-Entropy Loss

Cross-entropy loss is a way to measure how well a machine learning model's predicted probabilities match the actual outcomes. Imagine it as a penalty for being confident in the wrong answer or not confident enough in the right answer. If the model predicts the correct class with high confidence, the penalty is small; if it’s wrong or unsure, the penalty is large. The goal is to minimize this penalty during training, helping the model improve its accuracy and confidence in predictions. It’s commonly used in classification tasks to train models to distinguish between different categories effectively.