.

So one of the most commonly used packages is XGBoost, which
you can install from conda forge for example. It's
completely scikit-learn compatible. So you can import
XGBClassifier if you want you can use it with grid search or
with pipelines.

The cool thing is its fast. It supports missing values so
you don't need to do an imputation strategy, you can put
them directly into XGBoost. And it also supports multi-core.

The gradient boosting in scikit-learn is just sequential on
a single core which on most o