Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance

10 Dec 2015Amichai PainskySaharon Rosset

Recursive partitioning approaches producing tree-like models are a long standing staple of predictive modeling, in the last decade mostly as ``sub-learners'' within state of the art ensemble methods like Boosting and Random Forest. However, a fundamental flaw in the partitioning (or splitting) rule of commonly used tree building methods precludes them from treating different types of variables equally... (read more)

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