Join Keith McCormick for an in-depth discussion in this video Interactions, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Interactions and interaction…terms represent a big topic.…But when I'm working with a model…and I'm struggling to get better accuracy,…this is one of the first places that I look.…Interactions are when the relationship between…two variables depends on a third.…Since we're building predictive models,…that's normally gonna take the form…of our target variable, our binary,…and one of our inputs being influenced by another input.…
Let's take a look at a couple of examples.…So for instance here's an example…from my regression course in the library.…If we're trying to predict beginning salary…using education level, we notice a general trend…that as education level goes up,…beginning salary goes up as well.…But notice for the blue line which represents the men,…the line goes up more steeply,…and for the green line representing the women,…the line goes up less steeply.…
So if we're really gonna understand…the impact of education on beginning salary,…we have to also know that third variable, sex.…Let's take a look at another example,…
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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