The One Standard Error Rule for Model Selection¶
Chen, Y., & Yang, Y. (2021). The One Standard Error Rule for Model Selection: Does It Work?. Stats, 4(4), 868-892.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Regularized Model Selection
- Prefer parsimony at the margin. Where the validation curve is flat near its optimum, lean toward stronger penalization (e.g., the one-standard-error rule) to buy stability against noise.
This sourceDescribes the one-standard-error rule as choosing a more parsimonious model within one estimated standard error of the minimum cross-validation error.
- Prefer parsimony at the margin. Where the validation curve is flat near its optimum, lean toward stronger penalization (e.g., the one-standard-error rule) to buy stability against noise.
Verification¶
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Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
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Registry ID ref:acd5190d38ea · see in the full table