Join Keith McCormick for an in-depth discussion in this video Discriminant with two categories, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, let's discuss…a couple of technical issues to attend to…while you're watching me demonstrate…Discriminant Analysis on the Titanic data set.…First, typcically, Discriminant Analysis will operate…under listwise deletion, which means if anything's missing,…the entire row is dropped.…You don't know their age, you're gonna drop the case.…You don't know their fare, that row is dropped.…Some implementations will impute,…meaning that they're gonna replace the missing data…with some estimate.…
You don't know their age?…You can replace with an average of age.…There are also fancier versions of imputation.…Next,…all inputs are used…typically in linear Discriminant Analysis.…However, there is a technique, which we're gonna see,…called Stepwise Discriminant Analysis,…where it will choose the variables for you.…Finally, and this is important and might even be surprising,…remember that we're talking about…linear Discriminant Analysis,…this is for scale variables only.…
Your categorical variables will have to be transformed,…
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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