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Generating Compact Tree Ensembles via Annealing
Tree ensembles are flexible predictive models that can capture relevant
variables and to some extent their interactions in a compact and interpretable
manner. Most algorithms for obtaining tree ensembles are based on versions of
boosting or Random Forest. Previous work showed that boosting algorithms
exhibit a cyclic behavior of selecting the same tree again and again due to the
way the loss is optimized. At the same time, Random Forest is not based on loss
optimization and obtains a more complex and less interpretable model. In this
paper we present a novel method for obtaining compact tree ensembles by growing
a large pool of trees in parallel with many independent boosting threads and
then selecting a small subset and updating their leaf weights by loss
optimization. We allow for the trees in the initial pool to have different
depths which further helps with generalization. Experiments on real datasets
show that the obtained model has usually a smaller loss than boosting, which is
also reflected in a lower misclassification error on the test set.Comment: Comparison with Random Forest included in the results sectio
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