Data Science Algorithms in a Week - Second Edition

Analysis

We are given M=3 variables, according to which a feature can be classified. In a random forest algorithm, we usually do not use all three variables to form tree branches at each node. We only use a subset (m) of variables from M. So we choose m such that m is less than, or equal to, M. The greater m is, the stronger the classifier is in each constructed tree. However, as mentioned earlier, more data leads to more bias. But, because we use multiple trees (with a lower m), even if each constructed tree is a weak classifier, their combined classification accuracy is strong. As we want to reduce bias in a random forest, we may want to consider choosing an m parameter that is slightly less than M.

Hence, we choose the maximum number of ...