1 ▁negative ▁ ▁feature ▁ 2 ▁negative ▁ ▁feature ▁ 3 ▁negative ▁-> ▁class 2 ▁ ▁feature ▁ 3 ▁positive ▁-> ▁class 1 ▁ ▁feature ▁ 2 ▁positive ▁ ▁feature ▁ 3 ▁negative ▁-> ▁class 1 ▁ ▁feature ▁ 3 ▁positive ▁-> ▁class 2 ▁ ▁feature ▁ 1 ▁positive ▁ ▁feature ▁ 2 ▁negative ▁ ▁feature ▁ 3 ▁negative ▁-> ▁class 1 ▁ ▁feature ▁ 3 ▁positive ▁-> ▁class 2 ▁ ▁feature ▁ 2 ▁positive ▁ ▁feature ▁ 3 ▁negative ▁-> ▁class 2 ▁ ▁feature ▁ 3 ▁positive ▁-> ▁class 2 ▁ ▁The ▁tree ▁has ▁all ▁the ▁combinations ▁of ▁possible ▁leaves ▁to ▁express ▁the ▁full ▁rules et , ▁whereas ▁( feature 1 ▁positive , ▁feature 2 ▁negative ) ▁and ▁( feature 1 ▁negative , ▁feature 2 ▁positive ) ▁should ▁actually ▁join ▁to ▁the ▁same ▁rule . ▁This ▁leads ▁to ▁a ▁tree ▁with ▁too ▁few ▁samples ▁on ▁the ▁leaves . ▁A ▁two - stage ▁algorithm ▁can ▁effectively ▁merge ▁these ▁two ▁cases ▁by ▁giving ▁a ▁medium - high ▁probability ▁to ▁class 1 ▁if ▁feature 1 ▁or ▁( ex clus ive ) ▁feature 2 ▁is ▁negative . ▁The ▁second ▁class ifier ▁can ▁pick ▁up ▁this ▁higher ▁probability ▁and ▁make ▁a ▁decision ▁on ▁the ▁sign ▁of ▁feature 3 . ▁ ▁In ▁a ▁bias - var iance ▁decomposition , ▁cas c aded ▁models ▁are ▁usually ▁seen ▁as ▁lower ing ▁bias ▁while ▁raising ▁variance . ▁ ▁See ▁also ▁▁ ▁Bo ost ing ▁( meta - algorithm ) ▁ ▁Bootstrap ▁aggreg ating ▁ ▁References ▁ ▁S ources ▁▁▁▁▁▁▁ ▁Category :