Stacking Ensemble Machine Learning With Python

Last Updated on August 17, 2020

Stacking or Stacked Generalization is an ensemble machine learning algorithm.

It uses a meta-learning algorithm to learn how to best combine the predictions from two or more base machine learning algorithms.

The benefit of stacking is that it can harness the capabilities of a range of well-performing models on a classification or regression task and make predictions that have better performance than any single model in the ensemble.

In this tutorial, you will discover the stacked generalization ensemble or stacking in Python.

After completing this tutorial, you will know:

• Stacking is an ensemble machine learning algorithm that learns how to best combine the predictions from multiple well-performing machine learning models.
• The scikit-learn library provides a standard implementation of the stacking ensemble in Python.
• How to use stacking ensembles for regression and classification predictive modeling.

Let’s get started.

• Updated Aug/2020: Improved code examples, added more references.

Tutorial Overview

This tutorial is divided
To finish reading, please visit source site