One of the most important research topics in machine learning is the problem of how to lower the generalization error of a learning algorithm, either by reducing the bias or the variance (or both). A major complication of any attempt to reduce variance or bias (or both) is that the definitions of “bias” and “variance” of a classification rule are not as obvious as they are in regression. In fact, there have been several conflicting suggestions for the bias-variance decomposition for classification problems.
KeywordsLoss Function Random Forest Bootstrap Sample Generalization Error AdaBoost Algorithm
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