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What 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 solutio
by fluidcruft 7mo ago
What 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 7mo agoNo, the post is doing cross-validation to test predictive power directly. The error will not decompose as neatly then.
- fluidcruft 7mo agoWhy would they do that and where do you see evidence they did?
- kqr 7mo agoBecause it's a direct way to measure predictive power, and it says so: "We’ll use leave-one-out cross-validation"