learning_curve() with parallel processing (
n_jobs > 1) it wrongly returns
score_times as sums of their respective duration across all parallel jobs of
_fit_and_score() rather than a meaningful, let's say, average.
This wrong aggregation seems to be caused by
_aggregate_score_dicts() which is unpacking all parallel job times as a single vector.
One solution could be to average fit and score times per