Join Keith McCormick for an in-depth discussion in this video Bias-variance trade-off and overfitting, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Now let's talk about a topic…that can seem abstract at first,…but one can truly make the argument…that this issue of the bias-variance trade-off…is why a course like this makes sense,…and why we have to learn a dozen algorithms…for doing binary classification.…Let me explain.…High bias is the situation that you face…when your model is not flexible enough…to get sufficient signal.…
Now not getting enough signal,…I'm sure you can guess this,…it means the model's not accurate enough.…But what's this notion of flexibility?…Well, think about something…like linear discriminate analysis.…It can only fit linear relationships.…Not as flexible.…If you have curvilinearity, or variable interactions,…you have to act in order to allow your…linear discriminate analysis to do that.…Neural networks are inherently more flexible.…
But, they're black box and they're more complex.…In short, high bias is gonna be the situation…of underfitting.…High variance, on the other hand,…is when your model is too sensitive.…So it's picking up a lot of noise.…
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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