|Stochastic Systems Group|
Performance of Random Forests in the Low False Alarm and Low Missed Detection Regimes
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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