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From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods

From Trees to Forests and Rule Sets - A Unified Overview of Ensemble Methods

This video was recorded at 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose 2007. Ensemble methods are one of the most influential developments in Machine Learning over the past decade. They perform extremely well in a variety of problem domains, have desirable statistical properties, and scale well computationally. By combining competing models into a committee, they can strengthen "weak" learning procedures. This tutorial explains two recent developments with ensemble methods: Importance Sampling reveals "classic" ensemble methods (bagging, random forests, and boosting) to be special cases of a single algorithm. This unified view clarifies the properties of these methods and suggests ways to improve their accuracy and speed. Rule Ensembles are linear rule models derived from decision tree ensembles. While maintaining (and often improving) the accuracy of the tree ensemble, the rule-based model is much more interpretable. This tutorial is aimed at both novice and advanced data mining researchers and practitioners especially in Engineering, Statistics, and Computer Science. Users with little exposure to ensemble methods will gain a clear overview of each method. Advanced practitioners already employing ensembles will gain insight into this breakthrough way to create next-generation models. John Elder's lecture: In a Nutshell, Examples & Timeline Predictive Learning Decision Trees Giovanni Seni's lecture: Model Selection (Bias-Variance Tradeoff , Regularization via shrinkage) Ensemble Learning & Importance Sampling (ISLE) Generic Ensemble Generation Bagging, Random Forest, AdaBoost, MART Rule Ensembles Interpretation

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