Cross-entropy loss penalizes confident wrong predictions more heavily
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log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily
Cross-entropy loss penalizes confident wrong predictions more heavily
Cross-entropy loss measures the average number of bits needed to identify an event when coding is optimized for an estimated probability distribution, not the true distribution. This loss function increases the penalty for confident predictions that are wrong, as it compares the estimated probabilities against the true probabilities. The loss is higher when the predicted probability is far from the true probability, especially if the prediction is confident.
Example
If a model predicts a 90% chance of rain (p=0.9) when it doesn't rain (true p=0.1), the cross-entropy loss will be high due to the significant difference between the predicted and true probabilities.
Remember this
Understanding this characteristic of cross-entropy loss helps in designing better models that are less prone to overconfidence and can lead to more accurate predictions.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
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