Log loss, also known as cross-entropy or logarithmic loss, is a classification error metric commonly used to evaluate the performance of probabilistic classification models. It measures the difference between the predicted probabilities and the actual class labels. Log loss is particularly useful when dealing with models that output probabilities, such as logistic regression or neural networks.
I. Advantages of Log Loss:
1. Probabilistic Interpretation:
- Log loss considers the predicted probabilities rather than just the predicted class labels. This makes it suitable for models that provide probability estimates, allowing for a more nuanced evaluation of uncertainty.
2. Sensitivity to Prediction Confidence:
- Log loss penalizes confident but wrong predictions more heavily than less confident predictions. This is important in scenarios where the model’s confidence in its predictions is a crucial factor.
3. Continuity and Smoothness:
- Log loss is a smooth and continuous metric. It avoids discontinuities that can be present in metrics like accuracy when a small change in the model’s output results in a large change…
