Machine Learning

Ensemble Machine Learning Algorithms in Python with scikit-learn

Last Updated on August 28, 2020

Ensembles can give you a boost in accuracy on your dataset.

In this post you will discover how you can create some of the most powerful types of ensembles in Python using scikit-learn.

This case study will step you through Boosting, Bagging and Majority Voting and show you how you can continue to ratchet up the accuracy of the models on your own datasets.

Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Jan/2017: Updated to reflect changes to the scikit-learn API in version 0.18.
  • Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.
Ensemble Machine Learning Algorithms in Python with scikit-learn

Ensemble Machine Learning Algorithms in Python with scikit-learn
Photo by The United States Army Band, some rights reserved.

Combine Model Predictions Into Ensemble Predictions

The three most popular methods for combining the predictions from different models are: