A Data Scientist’s Guide to Ensemble Learning: Techniques, Benefits, and Code

Author(s): Souradip Pal

Originally published on Towards AI.

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Imagine you’re trying to guess how many jelly beans are in a jar. You ask five people individually, and their answers vary wildly. But when you average those guesses, suddenly, you’re close to the real number. This principle of collective intelligence — the “wisdom of the crowd” — is the foundation of Ensemble Learning in machine learning. By pooling together, the predictions of multiple models, just like gathering opinions from different individuals, we can often improve accuracy and robustness, much like how the average guess ends up being more reliable.

But here’s the twist: ensemble learning doesn’t just combine random models willy-nilly. Instead, it leverages a combination of different algorithms — or even the same algorithm applied to varied data or configurations — to make smarter decisions. Let’s dive into this fascinating concept and see how it works in the world of machine learning!

The real power of Ensemble Learning comes from diversity. Instead of relying on one model, ensemble methods build multiple models that may use:

Different algorithms: For example, one model might use Decision Trees while another uses Logistic Regression.The same algorithm but trained on different subsets of data: Even… Read the full blog for free on Medium.

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Published via Towards AI