35. How Many PCs to Use?

Question:

Now you'll experiment with keeping different numbers of principal components. In a multiclass classification problem like this one (more than 2 labels to apply), accuracy is a less-intuitive metric than in the 2-class case. Instead, a popular metric is the F1 score.

We’ll learn about the F1 score properly in the lesson on evaluation metrics, but you’ll figure out for yourself whether a good classifier is characterized by a high or low F1 score. You’ll do this by varying the number of principal components and watching how the F1 score changes in response.

As you add more principal components as features for training your classifier, do you expect it to get better or worse performance?