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LogitBoost

Boosting algorithm

Wikipedia contributorsAdapted for Fathomly · View authors
1 min read
Source updated September 13, 2026

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.

01Minimizing the LogitBoost cost function

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

f=\sum _{t}\alpha _{t}h_{t}

the LogitBoost algorithm minimizes the logistic loss:

\sum _{i}\log \left(1+e^{-y_{i}f(x_{i})}\right)
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This article is adapted from the Wikipedia article “LogitBoost”, written by its contributors and licensed under CC BY-SA 4.0. Fathomly has changed the layout, removed citation markers, navigation and maintenance notices, and adjusted punctuation. This adapted version is shared under the same license. For references, see the original article.

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