Random forests
Note
A single Decision Tree can learn quite complex functions. However, decision trees are prone to overfitting--learning rules that work only for the specific training set and don't generalize well to new data.
One of the ways that we can adjust for this is to limit the number of rules that it learns. For instance, we could limit the depth of the tree to just three layers. Such a tree will learn the best rules for splitting the dataset at a global level, but won't learn highly specific rules that separate the dataset into highly accurate groups. This trade-off results in trees that may have a good generalization, but an overall slightly poorer performance on the training dataset.
To compensate for this, we could create many of these limited decision trees and then ask each to predict the class value. We could take a majority vote and use that answer as our overall prediction. Random Forests is an algorithm developed from this insight.
There are two problems with the aforementioned...
