3 ms·
With one term it gets more robust in the face of excluding endpoints when constructing the jackknife train/test split, I think. But you're right, it does sound
by kqr 8mo ago
With one term it gets more robust in the face of excluding endpoints when constructing the jackknife train/test split, I think. But you're right, it does sound fishy.
- fluidcruft 8mo agoWhat the post is describing is just ANOVA. If removing a category improves the overall fit then fitting the two terms independently has the same optimal solution (with the two independent terms found to be identical). MSE never increases when adding a category. This is why you have to reach to things that penalize adding parameters to models when running model comparisons.
- kqr 8mo agoNo, the post is doing cross-validation to test predictive power directly. The error will not decompose as neatly then.
- fluidcruft 8mo agoWhy would they do that and where do you see evidence they did?
- kqr 8mo agoBecause it's a direct way to measure predictive power, and it says so: "We’ll use leave-one-out cross-validation"