Join Keith McCormick for an in-depth discussion in this video Data reduction, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, I wanna give you a brief introduction…to a big topic, data reduction.…You'll hear other phrases for this as well,…like feature selection.…So, what is it?…It's the removal of poor…and redundant predictors before modeling.…There's yet another phrase that gets at this,…sometimes you'll hear people talking about optimal subsets.…So, you've got your pool of variables…and are you choosing the right ones?…Now, as you know, algorithms differ in how they tackle this.…
Some tackle it directly and others don't,…which raises the following issue.…How much do we have to worry about this?…Aren't the algorithms taking care of it?…After all, we know that trees and stepwise logistic…as well as stepwise discriminate don't use…all the variables.…They're supposedly picking the best ones.…And what about a technique like neural nets?…Now, that uses all the variables,…so you would think this is more of an issue, perhaps,…but if you think it through,…what a lot of folks will say in response is,…the neural nets can simply assign near-zero weight,…
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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