Log Loss
The loss function used in binary logistic regression.
Plain English Explanation
The loss function used in binary logistic regression.
The following formula calculates Log Loss:
where: - \((x,y)\in D\) is the dataset containing many labeled examples, which are \((x,y)\) pairs. - \(y\) is the label in a labeled example. Since this is logistic regression, every value of \(y\) must either be 0 or 1. - \(y'\) is the predicted value (somewhere between 0 and 1, exclusive), given the set of features in \(x\). --- See Logistic regression: Loss and regularization in Machine Learning Crash Course for more information.
How is it used?
Practitioners refer to log loss when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.