Ensemble Learning for AI Developers: Learn Bagging, Stacking, and Boosting Methods with Use Cases

© Alok Kumar and Mayank Jain 2020

A. Kumar, M. JainEnsemble Learning for AI Developershttps://doi.org/10.1007/978-1-4842-5940-5_2

2. Mixing Training Data

Alok Kumar¹  and Mayank Jain¹

(1)

Gurugram, India

 

In Chapter 1, you learned how the role of a data scientist is similar to a concertmaster who uses his ensemble of orchestra and instruments to compose a beautiful composition. Similarly, a data scientist has multiple ensemble tools at his disposal if he wants to squeeze a world-class performance out of his data and models. In this chapter, the main goal is to learn different ways to mix training data to get ensemble models.

The following are the goals for this chapter.

• Build an intuitive understanding of how mixing training data can lead to good performance ...