Understanding the Random Forest Function Parameters in scikit-learn

What do the parameters in the Random Forest algorithm really mean?

About this article

In this article, we will try to get a deeper understanding of what each of the parameters does in the Random Forest algorithm. This is not an explanation of how the algorithm works. ( You might want to start with a simple explanation of how the algorithm works, found here in the link — A pictorial guide to understanding Random Forest Algorithm.)

A pictorial guide to understanding Random Forest Algorithm

Packages

The packages we will be looking at are

sklearn.ensemble.RandomForestClassifier

( for the Random Forest Classifier algorithm found in the sklearn library )

sklearn.ensemble.RandomForestRegressor

(for the Random Forest regressor algorithm)

Random Forest Classifier — parameters

1. n_estimators ( default = 100 )

Since the RandomForest algorithm is an ensemble modelling technique, it ‘increases the generalization’ by creating a number of different kinds of trees with…