Log loss

Log loss, also called logistic regression loss or cross-entropy loss, is defined on probability estimates.The metric looks at how well a model can classify a binary target, log loss evaluates how close a model’s predicted values (uncalibrated probability estimates) are to the actual target value. For example, does a model tend to assign a high predicted value like .80 for the positive class, or does it show a poor ability to recognize the positive class and assign a lower predicted value like .50? Log loss can be any value greater than or equal to 0, with 0 meaning that the model correctly assigns a probability of 0% or 100%.

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