### 问题

What is the difference between cross-entropy and log loss error? The formulae for both seem to be very similar.

### 回答1:

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:

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.

来源：`https://stackoverflow.com/questions/50913508/what-is-the-difference-between-cross-entropy-and-log-loss-error`