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What is the difference between cross-entropy and log loss error? The formulae for both seem to be very similar.
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Log Loss (Binary Cross-Entropy Loss): A loss function that represents how much the predicted probabilities deviate from the true ones. It is used in binary cases. Cross-Entropy Loss: A generalized form of the log loss, which is used for multi-class classification problems.
Maximizing the (log) likelihood is equivalent to minimizing the binary cross entropy. There is literally no difference between the two objective functions, so there can be no difference between the resulting model or its characteristics.
Cross-entropy loss function and logistic regression the logistic function as before. The logistic loss is sometimes called cross-entropy loss. It is also known as log loss (In this case, the binary label is often denoted by {−1,+1}).
Cross-entropy measures the performance of a classification model based on the probability and error, where the more likely (or the bigger the probability) of something is, the lower the cross-entropy.
They are essentially the same; usually, we use the term log loss for binary classification problems, and the more general cross-entropy (loss) for the general case of multi-class classification, but even this distinction is not consistent, and you'll often find the terms used interchangeably as synonyms.
From the Wikipedia entry for cross-entropy:
The logistic loss is sometimes called cross-entropy loss. It is also known as log loss
From the fast.ai wiki entry on log loss [link is now dead]:
Log loss and cross-entropy are slightly different depending on the context, but in machine learning when calculating error rates between 0 and 1 they resolve to the same thing.
From the ML Cheatsheet:
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1.
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