What is 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.
Why is log loss important?
Log loss is important because it quantifies how well a model's predicted probabilities align with the actual outcomes, helping to improve model accuracy.
How is log loss calculated?
Log loss is calculated using the formula: -1/n * Σ(y * log(p) + (1 - y) * log(1 - p)), where y is the true label and p is the predicted probability.
Can you give an example of log loss calculation?
Sure! If you have a true label of 1 and a predicted probability of 0.9, the log loss would be -log(0.9). If the true label is 0 and the predicted probability is 0.1, it would be -log(0.1).
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