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Anglický jazyk
Comparative study of set methods for classification
Autor: Marcel Katulumba Mbiya Ngandu
Ensemble methods are based on the idea of combining the predictions of several classifiers for a better generalization and to compensate for the possible defects of individual predictors.We distinguish two families of methods: Parallel methods (Bagging,... Viac o knihe
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O knihe
Ensemble methods are based on the idea of combining the predictions of several classifiers for a better generalization and to compensate for the possible defects of individual predictors.We distinguish two families of methods: Parallel methods (Bagging, Random forests) in which the principle is to average several predictions in the hope of a better result following the reduction of the variance of the average estimator.Sequential methods (Boosting) in which the parameters are iteratively adapted to produce a better mixture.In this work we argue that when the members of a predictor make different errors it is possible to reduce the misclassified examples compared to a single predictor. The performance obtained will be compared using criteria such as classification rate, sensitivity, specificity, recall, etc.
- Vydavateľstvo: Our Knowledge Publishing
- Rok vydania: 2022
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786204696737
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