From the course: Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference
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A/B testing during the evaluation phase
From the course: Machine Learning and AI Foundations: Prediction, Causation, and Statistical Inference
A/B testing during the evaluation phase
- [Instructor] Okay. So we've seen again and again, that statistics and machine learning are quite different and this is true but they can work together. If you look at CRISP-DM, you'll see that the phase after modeling is evaluation. I have to admit to a bit of a pet peeve. You'll hear folks say that the evaluation phase is running tests like R squared, area under the curve, overall accuracy. It's not true. That's actually modeling assessment. It's part of the modeling phase. The evaluation phase could be called business evaluation. It's not about the names. It's about this terribly important reminder that when you get to business evaluation, you're doing something very different than R squared or area under the curve. It's when you use business metrics to see if the model is performing well in a real world setting as a deployed model. This is a perfect opportunity to use statistics in support of your machine…
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