Understand stacking using scikit-learn

Discover the ability of stacking in machine learning — a method that mixes multiple models right into a single powerhouse predictor. This text explores stacking from its basics to advanced techniques, unveiling the way it blends the strengths of diverse models for enhanced accuracy. Whether you’re latest to stacking or in search of optimization strategies, this guide offers practical insights and suggestions to raise your predictive modeling game with scikit-learn.

While this text is predicated on scikit-learn, I provide at the tip a pure Python class that implements and mimics the stacking models of scikit-learn. Reviewing this pure Python implementation is a superb approach to confront and test your understanding.

On this post, we’ll see:

• how stacking is a component of ensemble techniques in ML
• how stacking works internally to supply predictions
• the way it is fitted
• what’s “restacking”
• how multi-layer stack will be created
• how and why we should always inspect the performance of the bottom models
• how one can tune and optimize the usage of stack models

If you happen to like or wish to learn machine learning with scikit-learn, try my tutorial series on this amazing package:

Sklearn tutorial

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Stacking is an ensemble technique in machine learning, meaning it combines several “base-models” right into a single “super-model”. Many alternative ensemble techniques exist and are a part of a few of one of the best performing techniques in traditional machine learning.

By “base-models”, I mean any traditional model you may have encountered — those you possibly can import, fit, and predict directly from scikit-learn. Those base models are for instance:

• linear regression or logistic regression (and…