Cross entropy

Concept from information theory commonly used in machine learning, particularly in classification problems. It measures the difference between two probability distributions for a given random variable or set of events, often serving as a loss function to evaluate model performance. Cross entropy is crucial for optimizing model parameters to improve prediction accuracy, benefiting data scientists and machine learning practitioners.

Cross entropy was discovered on April 22nd 2019 and it currently has a search volume of 18.1K with a growth of +4%.