In order to fully extract the power of ensembling, you need to learn the art of effectively applying it to real-world situations.
If you have heard of the 80/20 rule for data wrangling in machine learning, then you know that a vast amount of time is spent beyond searching and optimizing models. By the end of this chapter, you will have a good collection of reusable solutions to integrate ensembles into your real-world ML workflows.
The following are your learning goals for this chapter.
Feature selection using a random forest model. It should not ...
