Join Keith McCormick for an in-depth discussion in this video Confusion matrix, part of Machine Learning and AI Foundations: Classification Modeling.
- [Instructor] Okay, let's talk about he confusion matrix.…Now folks will joke about the name but,…the whole idea behind the name confusion matrix…is where the model is making errors.…And it's one of the most fundamental techniques…we have for assessing our model performance.…So let's take a look at the first thing you…should probably look at,…which is overall performance on the train…versus overall performance on the test.…
Here, overall performance on the train is 79.8%.…Overall performance on the test data is 74.2.…That's a gap of a bit more than 5%…which is just a hair outside the comfort zone.…Meaning that, we fear that the model isn't all that stable.…It's performance on unseen data…just isn't as strong as the performance on the data…that we fed to the modeling algorithm.…
It's a close call in this case…but it's at that borderline…and it's kind of on the wrong side of the border.…We want it to be within 5%.…So let's now talk about the wealth of data…that's hiding in here but focusing only on the test data.…
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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