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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.[1] 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[edit]

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:

See also[edit]


  1. ^ Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert (2000). "Additive logistic regression: a statistical view of boosting". Annals of Statistics. 28 (2): 337–407. CiteSeerX doi:10.1214/aos/1016218223.