Mini toolsDefinition
A performance measurement for classification models that outputs probabilistic values between 0 and 1.
Detailed Explanation
Also known as cross-entropy loss, log loss measures the performance of a model whose output is a probability value between 0 and 1. It increases as predicted probability diverges from actual label, severely penalizing predictions that are both confident and wrong.
Use Cases
Common in probabilistic classification tasks like risk assessment, disease probability prediction, and customer conversion prediction.
