Supporting materials for my Medium blogpost about ensemble learning.
Part 1: Combine your machine learning models for better out-of-sample accuracy
This part explores bootstrapping, as a theoretical foundation of ensemble learning.
The first part is coded out in bootstrapping.ipynb
Part 2: Digging deeper into ensemble learning
This part outlines the concepts of bagging and boosting, and how they help you get better out-of-sample fit for your Machine Learning model.
The second part is coded out in bagging-boosting.ipynb