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LogitBoost

From Wikipedia, the free encyclopedia

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

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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

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References

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  1. ^ Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert (2000). "Additive logistic regression: a statistical view of boosting". Annals of Statistics. 28 (2): 337–407. CiteSeerX 10.1.1.51.9525. doi:10.1214/aos/1016218223.
  2. ^ "Machine Learning Algorithms for Beginners". Retrieved 2023-10-01.