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simon
simon
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A classic question with an unclear answer, is it better to have an overfitted model performing better on a CVCross-Validation setting, or a non-overfitted model performing worse?

In this context, higher overfitting means higher discrepancy between train and test sets.

Overfitted: Avg Test AUROC 0.82 & Avg Train AUROC 0.96
Non-overfitted: Avg Test AUROC 0.78 & Avg Train AUROC 0.81

Which model should you use?

A classic question with an unclear answer, is it better to have an overfitted model performing better on a CV setting, or a non-overfitted model performing worse?

In this context, higher overfitting means higher discrepancy between train and test sets.

Overfitted: Avg Test AUROC 0.82 & Avg Train AUROC 0.96
Non-overfitted: Avg Test AUROC 0.78 & Avg Train AUROC 0.81

Which model should you use?

A classic question with an unclear answer, is it better to have an overfitted model performing better on a Cross-Validation setting, or a non-overfitted model performing worse?

In this context, higher overfitting means higher discrepancy between train and test sets.

Overfitted: Avg Test AUROC 0.82 & Avg Train AUROC 0.96
Non-overfitted: Avg Test AUROC 0.78 & Avg Train AUROC 0.81

Which model should you use?

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simon
simon
  • 133
  • 4

Overfitted model

A classic question with an unclear answer, is it better to have an overfitted model performing better on a CV setting, or a non-overfitted model performing worse?

In this context, higher overfitting means higher discrepancy between train and test sets.

Overfitted: Avg Test AUROC 0.82 & Avg Train AUROC 0.96
Non-overfitted: Avg Test AUROC 0.78 & Avg Train AUROC 0.81

Which model should you use?