How to Develop a Random Forest Ensemble in Python
Last Updated on September 7, 2020
Random forest is an ensemble machine learning algorithm.
It is perhaps the most popular and widely used machine learning algorithm given its good or excellent performance across a wide range of classification and regression predictive modeling problems.
It is also easy to use given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters.
In this tutorial, you will discover how to develop a random forest ensemble for classification and regression.
After completing this tutorial, you will know:
- Random forest ensemble is an ensemble of decision trees and a natural extension of bagging.
- How to use the random forest ensemble for classification and regression with scikit-learn.
- How to explore the effect of random forest model hyperparameters on model performance.
Let’s get started.
- Update Aug/2020: Added a common questions section.
How to Develop a Random Forest Ensemble in Python
Photo by Sheila Sund, some rights reserved.
Tutorial Overview
This tutorial is divided into four parts; they are: