I'm not sure that I like _inverse_label_binarize as a monolithic function, and should have a function like `best_labels` for the multiclass case, and `labels_above_threshold` in the multilabel case. Or something like that. (Note that there are other potential multilabel/retrieval-style discretisation strategies, such as getting the top `k` labels (akin to `best_label`), or those above a threshold relative to the top score for that sample.)

NB: It's not altogether relevant for this PR, but in the case of OvR with sparse labels, we shouldn't assume that the probabilities of all labels for all samples can fit contiguously in memory. So multilabel inverse transformers may need to have a mode in which they take an iterator of columns of the probability matrix.