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LogitBoost

From Quire, the open reference·Boosting algorithm·1 min read

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]

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

{\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)}

See also#edit

References#edit

  1. ^ Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert (2000). "Additive logistic regression: a statistical view of boosting". Annals of Statistics. 28 (2): 337–407. doi:10.1214/aos/1016218223.
  2. ^ "Machine Learning Algorithms for Beginners". 22 September 2023. Retrieved 2023-10-01.


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