LogitBoost

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.

Minimizing 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:

$\sum _i\log (1+e^{-y_{i}f(x_i)})$

See also

• Gradient boosting
• Logistic model tree

References

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