Join Keith McCormick for an in-depth discussion in this video So-called “black box” techniques, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] The modeling approaches…that we'll be discussing very greatly…and how transparent they are…during validation and interpretation.…This raises the issue of the so-called black box techniques.…A famous example is artificial neural networks.…The very same complexities…that often allow for very accurate models…can prevent you from sharing a story about the model.…
Sometimes something as basic as a key driver analysis.…Just simply a list of predictors…in order of importance can seem elusive.…Another easy to imagine example is an ensemble of trees.…A single decision tree is among the most transparent,…but a so-called random forest…is really a collection of trees.…Essentially an average of 100 or even 1,000 models…which then becomes difficult or impossible to interpret.…
The truth is that virtually all of these techniques…are in a continuum from transparent to opaque.…Perhaps the most transparent that we will see…in this course is logistic regression,…where all of the inputs can be ranked…from most to least important.…
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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