binary and multiclass classification the first classifier we sawthe threshold classifierwas simple binary classifier (the result is either one class or the other as point is either above the threshold or it is notthe second classifier we usedthe nearest neighbor classifierwas naturally multiclass classifier (the output can be one of several classesit is often simpler to define simple binary method than one that works on multiclass problems howeverwe can reduce the multiclass problem to series of binary decisions this is what we did earlier in the iris dataset in haphazard waywe observed that it was easy to separate one of the initial classes and focused on the other tworeducing the problem to two binary decisionsis it an iris setosa (yes or no)if nocheck whether it is an iris virginica (yes or noof coursewe want to leave this sort of reasoning to the computer as usualthere are several solutions to this multiclass reduction the simplest is to use series of "one classifier versus the rest of the classifiersfor each possible label lwe build classifier of the type "is this or something else?when applying the ruleexactly one of the classifiers would say "yesand we would have our solution unfortunatelythis does not always happenso we have to decide how to deal with either multiple positive answers or no positive answers iris setosairis virgiinicais iris virginica is iris setosa is iris versicolour alternativelywe can build classification tree split the possible labels in two and build classifier that asks "should this example go to the left or the right bin?we can perform this splitting recursively until we obtain single label the preceding diagram depicts the tree of reasoning for the iris dataset each diamond is single binary classifier it is easy to imagine we could make this tree larger and encompass more decisions this means that any classifier that can be used for binary classification can also be adapted to handle any number of classes in simple way