Understand stacking using scikit-learn
Discover the power of stacking in machine learning — a technique that combines multiple models into a single powerhouse predictor. This article explores stacking from its basics to advanced techniques, unveiling how it blends the strengths of diverse models for enhanced accuracy. Whether you’re new to stacking or seeking optimization strategies, this guide offers practical insights and tips to elevate your predictive modeling game with scikit-learn.
While this article is based on scikit-learn, I provide at the end a pure Python class that implements and mimics the stacking models of scikit-learn. Reviewing this pure Python implementation is an excellent way to confront and test your understanding.
In this post, we’ll see:
- how stacking is part of ensemble techniques in ML
- how stacking works internally to provide predictions
- how it is fitted
- what is “restacking”
- how multi-layer stack can be created
- how and why we should inspect the performance of the base models
- how to tune and optimize the use of stack models
If you like or want to learn machine learning with scikit-learn, check out my tutorial series on this amazing package:
Sklearn tutorial
All images by author.
Stacking is an ensemble technique in machine learning, meaning it combines several “base-models” into a single “super-model”. Many different ensemble techniques exist and are part of some of the best performing techniques in traditional machine learning.
By “base-models”, I mean any traditional model you might have encountered — those you can import, fit, and predict directly from scikit-learn. Those base models are for example:
- linear regression or logistic regression (and…
