Boosting Regression Trees. Here we will train a model to tackle a diabetes regression task. In boosting the B trees are small each has d 1 terminal nodes.
Boosting means that each tree is dependent on prior trees. For each tree the response ie Y variable is different. Compared with bagging boosting does not use bootstrapped data.
18 rows Boosted regression trees combine the strengths of two algorithms.
How this works is that you first develop an initial model called the base learner using whatever algorithm of your choice linear tree etc. Boosting means that each tree is dependent on prior trees. Then select all remaining variables except CATMEDV as Selected Variables. This notebook shows how to use GBRT in scikit-learn an easy-to-use general-purpose toolbox for machine learning in PythonWe will start by giving a brief introduction to scikit-learn and its GBRT interface.
