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After applying AdaBoost to the Sonar data, I obtain something like this.

I dont understand that phenomenon, can anyone enlighten me?

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    $\begingroup$ It appears to be related to how confident AdaBoost is in correctly classifying the observations. See Schapiro and Freund's book, p16 has some introductory remarks that point to later chapters. $\endgroup$ – Christoph Hanck Sep 4 '15 at 15:13
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Exactly this question is answered by Schapire and Freund, et. al. in their publication

Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee The Annals of Statistics Vol. 26, No. 5 (Oct., 1998), pp. 1651-1686

The takeaway from all their math is, that even when the training error is zero, the margin (= sample distance to decision boundary) is still improved by further boosting iterations. Also, among other things they show that there are lower upper bounds on the generalization error for a classifier with larger margin, which at least supports what you are seeing.

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