Cross entropy is a fundamental concept from information theory widely applied in machine learning, especially in classification tasks. It quantifies the difference between two probability distributions for a given random variable or set of events, serving as a loss function to assess model performance. By measuring the dissimilarity between the predicted and actual distributions, cross entropy aids in optimizing model parameters to enhance prediction accuracy. This technique is particularly beneficial for data scientists and machine learning practitioners seeking to improve their models’ effectiveness.
