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.^[2]

LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the form

${\displaystyle f=\sum _{t}\alpha _{t}h_{t))$

the LogitBoost algorithm minimizes the logistic loss:

${\displaystyle \sum _{i}\log \left(1+e^{-y_{i}f(x_{i})}\right)}$

• Gradient boosting
• Logistic model tree