Why Use Ensemble Learning?

Why Use Ensemble Learning?

Why Use Ensemble Learning? In this tutorial, you will discover the benefits of using ensemble methods for machine learning.

Ensembles are predictive models that combine predictions from two or more other models.

Ensemble learning methods are popular and the go-to technique when the best performance on a predictive modeling project is the most important outcome.

Nevertheless, they are not always the most appropriate technique to use and beginners the field of applied machine learning have the expectation that ensembles or a specific ensemble method are always the best method to use.

Ensembles offer two specific benefits on a predictive modeling project, and it is important to know what these benefits are and how to measure them to ensure that using an ensemble is the right decision on your project.

In this tutorial, you will discover the benefits of using ensemble methods for machine learning.

After reading this tutorial, you will know:

  • A minimum benefit of using ensembles is to reduce the spread in the average skill of a predictive model.
  • A key benefit of using ensembles is to improve the average prediction performance over any contributing member in the ensemble.
  • The mechanism for improved performance with ensembles is often the reduction in the variance component of prediction errors made by the contributing models.

Let’s get started.

ensemble learning

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