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Also known as: Classification loss
A classification loss measuring divergence between predicted probability distributions and true labels.
DESCRIPTION
Cross-entropy loss penalizes miscalibrated predictions by comparing predicted probabilities with true labels, aligning with maximum likelihood objectives. It is used for binary and multi-class tasks with logistic or softmax outputs. Variants like label smoothing and focal loss address overconfidence and class imbalance. Cross-entropy relates closely to KL divergence and is central to modern deep learning training.
ALTERNATIVE DEFINITIONS
scikit-learn
Log loss, also called logistic loss or cross-entropy loss, is a classification loss function.
Explicitly ties log loss to cross-entropy in classification contexts.
TensorFlow
Computes the crossentropy loss between the labels and predictions.
Implementation-focused description for categorical cross-entropy.
Wikipedia
In information theory, the cross entropy between two probability distributions measures the average number of bits needed to identify an event drawn from the set if a coding scheme is used based on a given probability distribution, rather than the “true” distribution.
Information-theoretic perspective highlighting coding interpretation.
RELATED TERMS
Softmax Negative Log-Likelihood Entropy KL Divergence Binary Cross-Entropy Label Smoothing Logistic Regression Calibration
COMPETING TERMS
Hinge Loss Mean Squared Error Focal Loss
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Cross entropy
- scikit-learn - API definition for log loss
- PyTorch - CrossEntropyLoss module documentation
- TensorFlow - Categorical crossentropy API docs
TOP QUESTIONS
What is Cross-Entropy Loss?
It is a loss function that penalizes incorrect predicted probabilities by comparing them to true labels, widely used in classification.
Category: Definition
Why is Cross-Entropy preferred over MSE for classification?
It aligns with probabilistic modeling and maximum likelihood, yielding better gradients for softmax/logistic outputs.
Category: Opinion
What is the difference between binary and categorical Cross-Entropy Loss?
Binary handles two classes with logistic outputs; categorical handles multi-class with softmax probabilities.
Category: Difference
How does Cross-Entropy relate to KL Divergence?
Cross-entropy equals entropy plus KL divergence; minimizing cross-entropy often corresponds to minimizing KL to the true distribution.
Category: Relation
What are common mistakes with Cross-Entropy Loss?
Incorrectly applying softmax twice, wrong label encoding (one-hot vs indices), and ignoring class imbalance are typical errors.
Category: Mistakes
How does label smoothing modify Cross-Entropy Loss?
It softens hard targets to reduce overconfidence, improving calibration and generalization.
Category: Example
What is a competing loss to Cross-Entropy?
Hinge loss (SVM), focal loss for imbalance, and MSE in specific settings are alternatives.
Category: Alternatives
How can I diagnose issues with Cross-Entropy training?
Check logits and probability outputs, ensure correct label encoding, and monitor calibration metrics like ECE.
Category: HowTo
What risks arise from poorly calibrated Cross-Entropy models?
Overconfident wrong predictions can mislead downstream decisions; consider calibration techniques or label smoothing.
Category: Risks
Where should I start learning about Cross-Entropy Loss?
Read library docs (PyTorch, TensorFlow), then explore information-theoretic foundations on cross-entropy and KL divergence.
Category: Resources