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Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Comment: Boosting Algorithms: Regularization, Prediction And Model Fitting, A. Buja, David Mease, A. Wyner
Faculty Publications
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology that illustrates how a fundamental innovation can penetrate every nook and cranny of statistical thinking and practice. They introduce the reader to one particular interpretation of boosting and then give a display of its potential with extensions from classification (where …
Boosted Classification Trees And Class Probability/Quantile Estimation, David Mease, A. Wyner, A. Buja
Boosted Classification Trees And Class Probability/Quantile Estimation, David Mease, A. Wyner, A. Buja
Faculty Publications
The standard by which binary classifiers are usually judged, misclassification error, assumes equal costs of misclassifying the two classes or, equivalently, classifying at the 1/2 quantile of the conditional class probability function P[y = 1jx]. Boosted classification trees are known to perform quite well for such problems. In this article we consider the use of standard, off-the-shelf boosting for two more general problems: 1) classification with unequal costs or, equivalently, classification at quantiles other than 1/2, and 2) estimation of the conditional class probability function P[y = 1jx]. We first examine whether the latter problem, estimation of P[y = 1jx], …