In this post we do several tasks performing the Bagging and the Random Forest Classificators.
We gradually develop classifier for the Bagging on randomized trees that in its final stage matches the Random Forest algorithm.
We’ll also build the RandomForestClassifier of sklearn.ensemble and learn of its quality depending on (1) number of trees, (2) max features used for each tree node, and (3) max tree depth.
Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models.
Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample.