In this video, learn how to evaluate your models effectively using a variety of checks and balances.
- When it comes to machine learning, … we're inclined to think that our prediction should be … accurate, above all, but is accuracy enough? … In some cases, only evaluating your model by its accuracy … can fool us into thinking we have a great model … when we actually don't. … So in machine learning, … accuracy is really just the share of all total predictions … that were correct. … Now let's consider an example. … Now, let's say you are looking to predict … the occurrence of bank fraud in your data, … you have a set of labeled training data, … and in 5% of records, we've identified fraud. … This means the remaining observations are not fraudulent. … Now, what if I told you I could create a predictive model … on this data that was 95% accurate? … Sounds pretty good, right? … Well, to do this, I could simply always predict … no bank fraud, and sure enough, … I'd be right 95% of the time. … This example of an imbalanced classification problem … is a good motivator for going beyond accuracy …
This course was created by Madecraft. We are pleased to host this training in our library.