  Tree-based methods are popular machine learning techniques used in various
fields. In this work, we review their foundations and a general framework the
importance sampled learning ensemble (ISLE) that accelerates their fitting
process. Furthermore, we describe a model combination strategy called the
adaptive regression by mixing (ARM), which is feasible for tree-based methods
via ISLE. Moreover, three modified ISLEs are proposed, and their performance
are evaluated on the real data sets.
