Join Keith McCormick for an in-depth discussion in this video Imbalanced target categories, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, let's talk about a very common problem,…and that's the problem of an imbalanced target variable.…So of course we're talking about binary classification,…so this is a concern when one of the categories…is more common than the other.…It's really when it's more dramatically out of balance.…And keep in mind that this is an issue…that potentially affects all the algorithms.…It's very easy to see in a decision tree,…so I'm gonna visualize it with you using a decision tree,…but it affects the others, as well.…
So with that in mind,…let's take a look at Titanic data.…We have 38.5% survival rate,…so conversely we have 61%, a little bit more,…of those that died.…In other words, our target variable is not 50% and 50%.…When is this a concern?…It's usually when it's more dramatic.…70%, 80%, 90% in one category.…A really dramatic one that happens quite regularly is fraud,…where you might have a ratio of 200 to one or 500 to one…or even more.…
So what does it look like when you balance your data?…It ends up looking like this.…
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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