Log Loss
Log Loss, also known as logarithmic loss or cross-entropy loss, is a performance metric used in machine learning to evaluate the accuracy of probabilistic predictions, particularly for classification models. It measures the difference between predicted probabilities and actual binary or categorical outcomes, with lower values indicating better model performance. This metric is widely applied in scenarios where confidence in predictions matters, such as spam detection or medical diagnosis.
Developers should learn and use Log Loss when building or tuning classification models, especially in binary or multi-class problems where probabilistic outputs are required, such as logistic regression or neural networks. It is crucial for optimizing models in competitions like Kaggle, as it penalizes incorrect predictions more heavily when the model is confident but wrong, encouraging well-calibrated probabilities. Use cases include fraud detection, where false positives and negatives have significant costs, and recommendation systems that rely on probability rankings.