What is the impact of log loss in machine learning?

What is the impact of log loss in machine learning?

Log loss, or logistic loss, measures the performance of a classification model whose output is a probability value between 0 and 1. A lower log loss indicates a better model.

How does log loss differ from accuracy?

While accuracy measures the proportion of correct predictions, log loss evaluates the probabilities assigned to each class, providing a more nuanced view of model performance.

Can you give an example of how to calculate log loss?

Sure! If you have predicted probabilities and actual labels, log loss is calculated using the formula: -1/N * Σ(y * log(p) + (1-y) * log(1-p)), where y is the actual label and p is the predicted probability.

What are the implications of high log loss?

High log loss indicates that the model is making poor predictions, often assigning high probabilities to incorrect classes, which can lead to significant misclassifications.