▁arbitrary ▁non - object ▁images . ▁The ▁time ▁constraint ▁in ▁training ▁a ▁cas cade ▁class ifier ▁can ▁be ▁circum vent ed ▁using ▁cloud - comput ing ▁methods . ▁ ▁C asc ading ▁class ifiers ▁in ▁statistics ▁▁ ▁The ▁term ▁is ▁also ▁used ▁in ▁statistics ▁to ▁describe ▁a ▁model ▁that ▁is ▁st aged . ▁For ▁example , ▁a ▁class ifier ▁( for ▁example ▁k - me ans ), ▁takes ▁a ▁vector ▁of ▁features ▁( dec ision ▁variables ) ▁and ▁outputs ▁for ▁each ▁possible ▁classification ▁result ▁the ▁probability ▁that ▁the ▁vector ▁belongs ▁to ▁the ▁class . ▁This ▁is ▁usually ▁used ▁to ▁take ▁a ▁decision ▁( class ify ▁into ▁the ▁class ▁with ▁highest ▁probability ), ▁but ▁cas c ading ▁class ifiers ▁use ▁this ▁output ▁as ▁the ▁input ▁to ▁another ▁model ▁( another ▁stage ). ▁This ▁is ▁particularly ▁useful ▁for ▁models ▁that ▁have ▁highly ▁combin atorial ▁or ▁counting ▁rules ▁( for ▁example , ▁class 1 ▁if ▁exactly ▁two ▁features ▁are ▁negative , ▁class 2 ▁otherwise ), ▁which ▁cannot ▁be ▁fitted ▁without ▁looking ▁at ▁all ▁the ▁interaction ▁terms . ▁Having ▁cas c ading ▁class ifiers ▁enables ▁the ▁success ive ▁stage ▁to ▁gradually ▁approximate ▁the ▁combin atorial ▁nature ▁of ▁the ▁classification , ▁or ▁to ▁add ▁interaction ▁terms ▁in ▁classification ▁algorithms ▁that ▁cannot ▁express ▁them ▁in ▁one ▁stage . ▁ ▁As ▁a ▁simple ▁example , ▁if ▁we ▁try ▁to ▁match ▁the ▁rule ▁( class 1 ▁if ▁exactly ▁ 2 ▁features ▁out ▁of ▁ 3 ▁are ▁negative , ▁class 2 ▁otherwise ), ▁a ▁decision ▁tree ▁would ▁be : ▁ ▁feature ▁