What does Cross-Entropy Loss mean? Definition | AI Understanding

Definition

A common objective function used to train classification models by penalizing incorrect probabilities.

Related terms

A mathematical objective that quantifies prediction error during training.

Human-added labels or metadata used to train or evaluate machine learning models.

Trusted reference labels used to train or evaluate model outputs.

How well a model's confidence scores match actual correctness probabilities.

A task where a model assigns an input to one or more predefined categories.

A model designed specifically for classification tasks.

Learn more in our free guides

Entropy-Based Sampling Focal Loss for Imbalanced Detection Triplet Loss and Metric Learning Siamese Networks and Triplet Loss

See also

Convolutional Neural Network (CNN)Data Augmentation Continual Learning Data Drift Context Window Data Labeling Computer Vision Dataset

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