Join Keith McCormick for an in-depth discussion in this video Ensembles, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] There is a frequent claim…that's made that not just some…but all of the kaggle.com winners…on the kaggle.com data mining modeling competition…website are ensembles.…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.…
So I don't think you want to assume on real world projects…that you'll always use an ensemble,…but there's a reason why they win these competitions,…they're very powerful.…So in its most basic form,…a so-called heterogeneous ensemble…would just be a combination of three models.…You could have a logistic regression,…a neural network and a support vector machine,…or any other combination of the algorithms…that we've talked about,…the notion of it being heterogeneous…is that we're not making an assumption here…that all three of these models are trees.…
They are a mix.…And we can simply calculate an average,…just as simple as that.…
Note: These tutorials are focused on the theory and practical application of binary classification algorithms. No software is required to follow along with the course.
- Why do you need classification?
- Statistical algorithms versus machine learning algorithms
- Combining models using ensembles
- Classification modeling challenges
Skill Level Intermediate
SPSS Statistics Essential Trainingwith Barton Poulson4h 57m Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
1. The Big Picture: Defining Your Classification Strategy
2. How Do I Choose a "Winner"?
3. Algorithms on Parade
4. Common Modeling Challenges
Next steps3m 17s
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