In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized additive model and then applies the cost function of logistic regression, one can derive the LogitBoost algorithm.
Minimizing the LogitBoost cost function
LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the form
the LogitBoost algorithm minimizes the logistic loss:
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