How to Develop Voting Ensembles With Python

Last Updated on September 7, 2020

Voting is an ensemble machine learning algorithm.

For regression, a voting ensemble involves making a prediction that is the average of multiple other regression models.

In classification, a hard voting ensemble involves summing the votes for crisp class labels from other models and predicting the class with the most votes. A soft voting ensemble involves summing the predicted probabilities for class labels and predicting the class label with the largest sum probability.

In this tutorial, you will discover how to create voting ensembles for machine learning algorithms in Python.

After completing this tutorial, you will know:

• A voting ensemble involves summing the predictions made by classification models or averaging the predictions made by regression models.
• How voting ensembles work, when to use voting ensembles, and the limitations of the approach.
• How to implement a hard voting ensemble and soft voting ensemble for classification predictive modeling.

Let’s get started.