Histogram-based Gradient Boosting Classification Tree.
This estimator is much faster than
for big datasets (n_samples >= 10 000).
This estimator has native support for missing values (NaNs). During
training, the tree grower learns at each split point whether samples
with missing values should go to the left or right child, based on the
potential gain. When predicting, samples with missing values are
assigned to the left or right child consequently. If no missing values
were encountered for a given feature during training, then samples with
missing values are mapped to whichever child has the most samples.
This implementation is inspired by
This estimator is still experimental for now: the predictions
and the API might change without any deprecation cycle. To use it,
you need to explicitly import ``enable_hist_gradient_boosting``::
>>> # explicit...