Hands-On Data Science and Python Machine Learning

K-fold cross-validation to avoid overfitting

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 ...