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Is cross-entropy a performance metric?
However, the problem with cross-entropy, is that it doesn’t live on any objective scale, it’s a very relative metric. You can compare the performance of XGBoost Vs a Neural Network on a given data set and the one with a lower cross-entropy (or higher test log-likelihood) is the better model.
What is cross-entropy metric?
Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. Cross-entropy can be used as a loss function when optimizing classification models like logistic regression and artificial neural networks.
Cross-entropy is also related to and often confused with logistic loss, called log loss. Although the two measures are derived from a different source, when used as loss functions for classification models, both measures calculate the same quantity and can be used interchangeably.
Which is an example of a cross entropy function?
Cross-entropy is widely used as a loss function when optimizing classification models. Two examples that you may encounter include the logistic regression algorithm (a linear classification algorithm), and artificial neural networks that can be used for classification tasks.
How to calculate cross entropy in binary classification?
The cross-entropy for a single example in a binary classification task can be stated by unrolling the sum operation as follows: H (P, Q) = – (P (class0) * log (Q (class0)) + P (class1) * log (Q (class1))) You may see this form of calculating cross-entropy cited in textbooks.
Cross-entropy is related to divergence measures, such as the Kullback-Leibler, or KL, Divergence that quantifies how much one distribution differs from another. Specifically, the KL divergence measures a very similar quantity to cross-entropy.