Cross-Validation
Machine Learning
A loss function measuring the difference between predicted probability distributions and actual distributions.
Detailed Explanation
Cross-Entropy Loss quantifies the difference between two probability distributions, typically the model's predicted probabilities and the true labels. It calculates how well the predicted distribution matches the actual outcomes, with lower values indicating better predictions. Commonly used in classification tasks, it helps optimize models by penalizing incorrect predictions and encouraging probability outputs closer to the true labels.
Use Cases
- •Optimizing a neural network for image classification by minimizing cross-entropy loss to improve label prediction accuracy.
Related Terms
Other terms in the Machine Learning category