Understanding multiclass log-loss I'm trying to understand the multiclass log-loss as described in sk-learns documentation.
The wording ' Let the true labels (Y) for a set of samples be encoded as a 1-of-K binary indicator matrix...', implies to me that we have K different matrices, one for each class, the elements of which take values of 0 or 1 indicating the presence of that class (a kind of one-hot coding?).
Or is it that Y is a K by N matrix, N the number of samples? This makes far more sense to me, maybe I'm just misinterpreting the wording.
Can anyone point me to a more thorough explanation of the multi-class case? I can find plenty on the binary case, not so much multi-class.

Note what follows in the documentation after what you quote:

Let the true labels for a set of samples be encoded as a $1$-of-$K$ binary indicator matrix $Y$, i.e., $y_{i,k}=1$ if sample $i$ has label $k$ taken from a set of $K$ labels.

This is standard matrix nomenclature: $Y$ is a matrix with $N$ rows and $K$ columns (not $K$ by $N$ as you suspect), with the $i$-th row encoding the class membership of the $i$-th sample with a single $1$ at position $k$.
Incidentally, you could test your understanding by creating a small toy example, say with $N=2$ and $K=3$, and feeding it to the log_loss() function. It should quickly become apparent that $Y\in\mathbb{R}^{N\times K}$, not $Y\in\mathbb{R}^{K\times N}$ or something even stranger.
