Question

How do we know when LDA or QDA performs better on the test or training set?

A) Use cross-validation

B) Compare prediction errors

C) Assess model assumptions

D) Analyze feature importance

Answer 1

To determine whether LDA or QDA performs better, you can use cross-validation, compare prediction errors, and assess model assumptions.

Step-by-step explanation:

In order to determine whether LDA or QDA performs better on the test or training set, there are several methods you can use:

1. Use cross-validation: This involves dividing the data into training and validation sets and calculating the performance of both LDA and QDA on the validation set.
2. Compare prediction errors: Calculate the prediction errors for both methods on the test set and compare them. The method with lower prediction errors generally performs better.
3. Assess model assumptions: Check if the assumptions of LDA or QDA are violated in the data. If one method's assumptions are violated, the other method may be a better choice.

By using these methods, you can determine which method performs better on the test or training set.