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Different types of errors have different costs in most decision-making problems. In binary classification, there are two types of errors: false alarms and missed detections. We present new analysis of the generalization error of the random forest, a state-of-the-art ensemble classifier, that takes the two types of errors into account. The theoretical analysis is supported by comparison to empirical classification performance, and suggests a design principle for improving performance in either the low false alarm regime or the low missed detection regime.

Joint work with Ryan Prenger, Barry Chen, Tracy Lemmond, and Bill Hanley.

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