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Binary Cross Entropy

Binary Cross Entropy (or log loss) measures the performance of a binary classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. So predicting a probability of .012 when the actual observation label is 1 would be bad and result in a high loss value. A perfect score would have a log loss of 0.

\[ Binary\ Cross\ Entropy = -\frac{1}{N}\sum_{i=1}^N[y_i\log(p_i) + (1-y_i)\log(1-p_i)] \]

Parameters

This cost function does not have any parameters.

Example

use Rubix\ML\NeuralNet\CostFunctions\BinaryCrossEntropy;

$costFunction = new BinaryCrossEntropy();