Introduction to Hyperparameter Tuning

Let's take a look at how we can use SageMaker to improve our Boston housing model. To begin with, we will remind ourselves how we train a model using SageMaker.

Essentially, tuning a model means training a bunch of models, each with different hyperparameters, and then choosing the best performing model. Of course, we still need to describe two different aspects of hyperparameter tuning:

1) What is a bunch of models? In other words, how many different models should we train?

2) Which model is the best model? In other words, what sort of metric should we use in order to distinguish how well one model performs relative to another.