You are given a data set with m points and an algorithm that satisfies the weak$-$learning condition $($it always outputs a classifier with accuracy $60\%).$ Each classifier output by the weak$-$learning algorithm can be encoded using two bits. How can you construct a classifier that can be described by less than m bits and is correct on every data point in the data set $($you may assume m is very large$).$ What is the size of your final classifier?