The reweighted error is one half
Theorem taught in Ensembles, random forests, and boosting, part of Statistical learning
The reweighted error is one half
| Type | Theorem |
| Taught in | Ensembles, random forests, and boosting |
| Part | Statistical learning |
If 0<\varepsilon_t<1 and the current weights sum to 1, the updated weights give f_t weighted error exactly 1/2. For the usual positive-vote round, require 0<\varepsilon_t<1/2. At zero error the logarithm has no finite value: stop, or handle the perfect learner explicitly.
Used in
- Taught in Ensembles, random forests, and boosting, where the derivation and examples are.
- All 7 questions on Ensembles, random forests, and boosting
See also
Squared error of an average · Random forest · AdaBoost · Why averaging helps · Ensemble error decomposition · Where disagreement comes from · Bagging and random forests · Why decorrelating the trees helps