{"product_id":"comparative-study-of-set-methods-for-classification-von-marcel-katulumba-mbiya-ngandu","title":"Comparative study of set methods for classification","description":"\u003cp\u003eEnsemble 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.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9786204696737\"\u003e\u003ch3\u003eApplication of Adaboosting and Random Forest to Binary and Multi-class databases\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9786204696737","offer_id":40215025287261,"sku":"9786204696737","price":43.9,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/b8abfb29-18e6-4fcb-969c-8f7c030343da.png?v=1758084454","url":"https:\/\/shop.autorenwelt.de\/en\/products\/comparative-study-of-set-methods-for-classification-von-marcel-katulumba-mbiya-ngandu","provider":"Autorenwelt Shop","version":"1.0","type":"link"}