Earlier in the book, we talked about train and test as a good way of preventing overfitting and actually measuring how well your model can perform on data it's never seen before. We can take that to the next level with a technique called k-fold cross-validation. So, let's talk about this powerful tool in your arsenal for fighting overfitting; k-fold cross-validation and learn how that works.
To recall from train/test, the idea was that we split all of our data that we're building a machine learning model based off of into two segments: a training dataset, and a test dataset. The idea is that we train our model only using the data in our training dataset, and then we evaluate its performance using the data that we reserved for our test dataset. That prevents us from overfitting to the data that we have because we&apos...
