We have seen how feature selection works with L1-regularized logistic regression in one of the previous sections, where weights of unimportant features are compressed to close to, or exactly, 0. Besides L1-regularized logistic regression, random forest is another frequently used feature selection technique.
To recap, random forest is bagging over a set of individual decision trees. Each tree considers a random subset of the features when searching for the best splitting point at each node. And, as an essence of the decision tree algorithm, only those significant features (along with their splitting values) are used to constitute tree nodes. Consider the forest as a whole: the more frequently a feature is used in a tree node, the more important it is. In other words, we can rank the importance of features based on their occurrences in nodes...
