<p dir="auto">The output shape for multi-label predict_proba is inconsistent :-(<br>
The OneVsRest classifier produces (n_samples, n_classes) (like multi-class)<br>
where for example RandomForestClassifier produces a list of probabilities per label, which are each (n_samples, 2) or (n_samples, 1).</p>
<p dir="auto">Not sure how to do a deprecation for that.<br>
But we should do a common test for whether a classifier supports multi-label and what the output shapes are.</p>
<p dir="auto">The <code class="notranslate">decision_function</code> and <code class="notranslate">predict_proba</code> of a multi-label classifier (e.g. <code class="notranslate">OneVsRestClassifier</code>) is a 2d arrays where each column correspond to a label and each row correspond to a sample. (added in 0.14?)</p>
<p dir="auto">The <code class="notranslate">decision_function</code> and <code class="notranslate">predict_proba</code> of multi-output multi-class classifier (e.g. <code class="notranslate">RandomForestClassifier</code>) is a list of length equal to the number of output with a multi-class decision_function or predict_proba output (a 2d array where each row corresponds to the samples and where each columns correspond to a class).</p>
<p dir="auto">So this means that multi-output problem with only binary class output is a multi-label task, but isn't consistent with the multi-label format...</p>
<p dir="auto">This is problematic if you want to code a <code class="notranslate">roc_auc_score</code> function to support multi-label output.</p>