Join Keith McCormick for an in-depth discussion in this video Stepwise discriminant, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, now we're gonna talk…about a different flavor of discriminant analysis…called stepwise discriminant analysis.…The whole idea is to let the stepwise discriminant…choose our variables for us.…So I've chosen a wider selection of variables:…age, passenger class, embarked, gender,…sibling/spouse, parent/child, and fare.…Now, stepwise is a bit controversial…within statistical circles, but it's helpful…to have methods that narrow down those variables.…
The complaint is that sometimes the list…that's chosen on the training data is not optimal…on the test data, but let's give it a try…and see how it goes.…So the first thing to talk about is which ones…did it choose in this case.…I actually ran all those variables…on the Titanic data set and it went with age,…it kept passenger class, but it did not choose embarked,…it went with gender and sibling/spouse,…but it did not go with parent/child and fare.…
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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