stake in the middle. This is the decision
tree of depth two. Then I fit the model to the residuals
here. To minimize the log loss of this dataset changes
something in the middle, and so on. And you can see that as
I add more and more estimator it fits the data better and
better and gets more and more complex decision boundaries.

Since it works like a gradient descent on log loss, it's a
little bit harder to visualize this for classification.

White mean probability of 0.5 (which is a tie). Red means
high p