8
Model Inference and Averaging
.1 Int roclnction
For most of this book, the fitting (learning) of models has been achieved by
minimizing a sum of squares for regression, or by minimizing cross-entropy
for classification. In fact, both of these minimizati
10
Boosting and Additive Trees
10.1
B
lUg
1 th 1
Boosting is one of the most powerful lea,rning ideas introduced in the last
ten years. It was originally designed for classification problems, but as will
be seen in this chapter, it can profitably be exten
9
Additive Models, Trees, and Related
Methods
In this chapter we begin our discussion of some specific methods for supervised learning. These techniques each assume a (different) structured form
for the unknown regression function, and by doing so they fi
splitting variable
splitting point based on
the non missing ovservations
non-missing variable
xj xk have non linear corelation, use xk to split xj for the
whole region is not reasonable, but locally Rm is good