Join Keith McCormick for an in-depth discussion in this video Binary vs. multinomial, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] I always caution folks…against trying to predict into more than two categories.…It would be an oversimplification…to suggest that you should never do it,…but it is an inherently advanced modeling challenge.…First, one's a priori estimate of accuracy is lower…"A priori" is a Latin phrase…which means, "Without the benefit of experience."…The phrase is frequently used in statistics.…If half of our target cases are true,…and the other half are false,…our chances of predicting that correctly…without a model is 50%.…
If we're predicting into three, or four, or five categories…our chances of guessing correctly…without a model is steadily going down.…If our chance is only 20%,…how good a job can we expect to do?…Even if we double our chances which is pretty impressive,…that brings us to 40% right and 60% wrong.…Predicting into more than a few is probably a fool's errand.…Perhaps you could get a way with…about a eight in rare circumstances,…but a multiple category in the target just won't…be good enough to solve whatever business challenge…
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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