<p dir="auto">I was running sklearn.svm.SVC, and it took five hours on the data I was using.  The results were good, but I think users should get an estimated time for the length of the program's run.  For instance, I know that keras has an ETA feature.</p>
<p dir="auto">In <code class="notranslate">_parallel_build_trees</code>, if the <code class="notranslate">bootstrap</code> option is selected, then <a href="https://github.com/scikit-learn/scikit-learn/blob/8edd9f9f1597094a1f8024f4d091f21e9c1fa66a/sklearn/ensemble/forest.py#L101">a bootstrap sample the size of the training set <code class="notranslate">X</code> is produced and used</a>.</p>
<p dir="auto">In the situation where <code class="notranslate">X</code> is quite big and a large number of estimators are to be fit, I would find it useful to limit the size of the bootstrap subsample to be less than the number of examples, having the loose expectation that the random subsamples chosen over all the estimators generally "cover" the training dataset.</p>
<p dir="auto">This seems like it could be implemented as a keyword argument <code class="notranslate">bootstrap_subsample_fraction</code> to the appropriate classes, taking the range of values <code class="notranslate">(0, 1]</code>.</p>
<p dir="auto">If this seems like a useful feature more broadly, I could take a whack at it.</p>