From the course: Machine Learning and AI Foundations: Classification Modeling

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Heterogenous ensembles

Heterogenous ensembles

From the course: Machine Learning and AI Foundations: Classification Modeling

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Heterogenous ensembles

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- [Instructor] Ensembles are a general technique involving combining component models to produce a new model, and the concept has inspired several important algorithms, but you can think of ensembles as an algorithm of their own. So let's take a moment to talk about ensembles. Now, keep in mind when you take a bunch of models and combine them, you've just turned your model into something more complex and almost certainly into something opaque. Even if the component models are explainable, the ensemble might not be. So I don't think you want to assume on real world projects that you'll always have the option of using an ensemble, but there's a reason why they win so many machine learning competitions like the ones on kaggle.com. They're very powerful and are frequently the most accurate when compared to the single model algorithms that we've discussed so far. So in its most basic form, a so-called heterogeneous ensemble…

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