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Evidence Contrary to the Statistical View of Boosting

Evidence Contrary to the Statistical View of Boosting. David Mease & Abraham Wyner. What is the Statistical View? . The idea presented in J . Friedman, T. Hastie, and R. Tibshirani . Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:337–374, 2000a

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Evidence Contrary to the Statistical View of Boosting

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  1. Evidence Contrary to the Statistical View of Boosting David Mease & Abraham Wyner

  2. What is the Statistical View? • The idea presented in • J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:337–374, 2000a • Ada Boost is similar to LogitBoost Regression

  3. The Challenge • Centers on areas where the paper’s view fails to explain important characteristics of Ada Boost • The statistical view of boosting focuses only on one aspect of the algorithm - the optimization. • Does not explain why the statistical view doesn’t work, merely presents evidence to the contrary

  4. Areas of Deficiency for Statistical View • Stagewisenature of the algorithm • Empirical variance reduction that can be observed on hold out samples • variance reduction seems to happen accidently. • Strong resistance to over fitting of Ada Boost which is lost in regression model

  5. Practical Advice • AdaBoost is one of the most successful boosting algorithms • Do not assume that newer, regularized and modified versions of boosting are necessarily better • Try standard AdaBoost along with these newer algorithms • If classification is the goal, monitor the misclassification error on hold out (or cross-validation) samples • Much of the evidence presented is counter-intuitive • keep an open mind when experimenting with AdaBoost • If stumps are causing overfitting, be willing to try larger trees • Intuition may suggest the larger trees will overfit, but we have seen that is not necessarily true

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