Random Forest
In this lesson, we show how to use the Random Forest, which is an ensemble of models.
We'll cover the following...
What is Random Forest?
In the last lesson, we talked about one of the main ensemble methods - boosting
. In this lesson, we would talk about another main ensemble method - bagging
.
Bagging
is not a particular model, it’s a method to improve your predictive ability based on your existing models. It builds lots of independent models, most of which are weak model or estimators, that can be of the same type or different. They can use the same feature sets or not. The core point of bagging
is to combine the output of different models. If it’s a classification, they are voting for the results.
Random forest
is a typical example of them, which uses a lot of decision trees as a base(weak) estimator. Each tree randomly chooses a feature set and instances from the training data. According to the learning theory, bagging
can reduce the variance of the model. Individual decision trees typically exhibit ...