[source] # Cross Entropy Cross Entropy (or *log loss*) measures the performance of a classification model whose output is a joint probability distribution over the possible classes. Entropy increases as the predicted probability distribution diverges from the actual distribution. $$ Cross Entropy = -\sum_{c=1}^My_{o,c}\log(p_{o,c}) $$ ## Parameters This cost function does not have any parameters. ## Example ```php use Rubix\ML\NeuralNet\CostFunctions\CrossEntropy; $costFunction = new CrossEntropy(); ```