Join Keith McCormick for an in-depth discussion in this video KNN, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Of all the techniques…that we're gonna discuss…K- Nearest Neighbors is arguably…the most straightforward conceptually.…It's actually kind of fun talking about K- Nearest Neighbors…but it can also be quite effective.…So K-Nearest Neighbors is a so called lazy learner…and makes it quite different from the other choices.…No model per se is built.…Basically what's happening is that we have a technique…that although memory intensive…is not computationally intensive at model building at all…because it doesn't build a model.…
It simply memorizes the locations of all the cases.…Now at scoring when you go to deploy this thing…then it has to find all the nearest neighbors…and that then takes some work…So it kind of turns the typical process on its head.…It's virtually instant at model building…but then at scoring it can be a little bit slower…at scoring than some other techniques.…And that's the notion of a "Lazy" learner.…Also the notion of nearest is essentially Euclidean distance…Now it's not the only choice but it's the typical choice…
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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