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Ensemble Methods “No free lunch theorem” Wolpert and Macready 1995 “No free lunch theorem” Wolpert and Macready 1995 Solution search also involves searching for learners Different algorithms Different algorithms Different parameters Different algorithms Different parameters Different input representations/features Different algorithms Different parameters Different input representations/features Different data Base learner Diversity over accuracy Model combination Voting Bagging Boosting Cascading Data set = [1,2,3,4,5,6,7,8,9,10] Samples: Input to learner 1 = [10,2,5,10,3] Input to learner 2 = [4,5,2,7,6,3] Input to learner 3 = [8,8,4,9,1] Create complementary learners Create complementary learners Train successive learners on the mistakes of predecessors Weak learners combine to a strong learner Adaboost – Adaptive Boosting Adaboost – Adaptive Boosting Allows for a smaller training set Adaboost – Adaptive Boosting Allows for a smaller training set Simple classifiers Adaboost – Adaptive Boosting Allows for a smaller training set Simple classifiers Binary Modify probability of drawing examples from a training set based on errors Step 3 1 1 error 1 log( ) 2 error error 0.33 1 1 .33 1 log( ) 2 .33 1 0.35 Demo Sequence classifiers by complexity Sequence classifiers by complexity Use classifier j+1 if classifier j doesn’t meet a confidence threshold Sequence classifiers by complexity Use classifier j+1 if classifier j doesn’t meet a confidence threshold Train cascading classifiers on instances the previous classifier is not confident about Sequence classifiers by complexity Use classifier j+1 if classifier j doesn’t meet a confidence threshold Train cascading classifiers on instances the previous classifier is not confident about Most examples classified quickly, harder ones passed to more expensive classifiers Boosting and Cascading Object detection/tracking Collaborative filtering Neural networks Optical character recognition ++ Biometrics Data mining Ensemble methods are proven effective, but why?