Speed regression in random forests for predicting > 5k rows + clarify docs for n_jobs

It seems that for predicting less than 5k rows most of the time is in the fork, but for more rows, most of the time is in the actual predict, highly dependent on a bunch of parameters. The slowdown for predicting millions of rows in parallel is worse than the slowdown for predicting a few rows, so parallel should be the default imho. To get the other behavior, you can just set ``n_jobs=1`` before calling ``predict``.

I tried to run the ``scikit-learn-speed`` project but it relies on some version of ``vbench`` that I can't seem to find. Specifically, the classes ``PythonBenchmark`` and ``LineProfilerBenchmarkMixin`` are missing. I would like to add some more tests for random forests so we have tests on varying datasets and can track performance in the future.