Lecture 22 Ensemble

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Class Website CX4242: Ensemble Learning Mahdi Roozbahani Lecturer, Computational Science and Engineering, Georgia Tech Which Classifier/Model to Choose? • Possible strategies: • Go from simplest model to more complex model until you obtain desired accuracy • Discover a new model if the existing ones do not work for you • Combine all (simple) models 2 Common Strategy: Bagging (Bootstrap Aggregating) Originally designed for combining multiple models, to improve classification “stability” [Leo Breiman, 94] Uses random training datasets (sampled from one dataset) http://statistics.about.com/od/Applications/a/What-Is-Bootstrapping.htm Common Strategy: Bagging (Bootstrap Aggregating) Consider the data set 푆 = 푋푖, 푌푖 푖=1,…,푛 • Pick a sample S* with replacement of size n (S* called a “bootstrap sample”) • Train on S* to get a classifier f * • Repeat above steps B times to get f1, f2,...,fB • Final classifier f(x) = majority{fb(x)}j=1,...,B http://statistics.about.com/od/Applications/a/What-Is-Bootstrapping.htm Common Strategy: Bagging Why would bagging work? • Combining multiple classifiers reduces the variance of the final classifier When would this be useful? • We have a classifier with high variance Bagging decision trees Consider the data set S • Pick a sample S* with replacement of size n • Grow a decision tree Tb • Repeat B times to get T1,...,TB • The final classifier will be Random Forests Almost identical to bagging decision trees, except we introduce some randomness: • Randomly pick m of the d available attributes, at every split when growing the tree (i.e., d - m attributes ignored) Bagged random decision trees = Random forests What are our Hyper-Parameters in Random Forest 푚 = 푁푢푚푏푒푟 표푓 푟푎푛푑표푚푙푦 푐ℎ표푠푒푛 푎푡푡푟푏푢푡푒푠 Usual values for 푚 = 푑, 1,10 푑 is number of dimensions or features or attributes How to optimize 푚? Cross-Validation 퐵 = 푁푢푚푏푒푟 표푓 푚표푑푒푙푠 표푟 푑푒푐푠표푛 푡푟푒푒푠 푛 푅푎푛푑표푚 퐹표푟푒푠푡 Keep adding trees until training error stabilizes (reaches to a plateau).

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