交叉熵和对数损失误差有什么区别？
What is the difference between cross-entropy and log loss error?
交叉熵和对数损失误差有什么区别？两者的公式似乎非常相似。
它们本质上是一样的；通常，我们使用术语 log loss 来表示二进制 classification 问题，而更一般的 cross-entropy (loss) multi-class class化的一般情况，但即使这种区别也不一致，您会经常发现这些术语可互换用作同义词。
来自Wikipedia entry for cross-entropy：
The logistic loss is sometimes called cross-entropy loss. It is also known as log loss
来自fast.ai wiki entry on log loss[link现已死亡]：
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.
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1.
交叉熵和对数损失误差有什么区别？两者的公式似乎非常相似。
它们本质上是一样的；通常，我们使用术语 log loss 来表示二进制 classification 问题，而更一般的 cross-entropy (loss) multi-class class化的一般情况，但即使这种区别也不一致，您会经常发现这些术语可互换用作同义词。
来自Wikipedia entry for cross-entropy：
The logistic loss is sometimes called cross-entropy loss. It is also known as log loss
来自fast.ai wiki entry on log loss[link现已死亡]：
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.
Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1.