Log Loss as Evaluation Metric

Hard

machine-learning Airbnb Netflix Lyft

Multiple Choice

Classifier B is better because higher log loss indicates more confident predictions Classifier A produces better-calibrated probability estimates, assigning higher confidence to correct predictions Log loss is unreliable when both models have the same accuracy The difference is meaningless since accuracy is the true measure of performance

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