Quantile Regression With Gradient Boosted Trees
When we do simple descriptive data exploration, we are seldom content with analyzing mean values only. More often, we take a more detailed look at the distribution, have a look at histograms, quantiles, and the like. Mean values alone often lead to erroneous conclusions, and keep important information hidden. But if this is the case, why do we forget about this as soon as we build predictive models? These usually aim only at mean values - and they lie.