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RCOMM 2010 Cross-validation
Transcript of RCOMM 2010 Cross-validation
Cross-validation Leave-one-out K = 5 K = N easy to use
easy to misuse Performance estimation predict future performance
model selection DM model £ $ € % DM model 1
DM model 2
DM model 3
... Best model Using global information Using future data Picking the best Feature selection Cross-validation % Time series Cross-validation 100% 100% Cross-validation Pick the best 62% ... ... ~50% Optimization CHECKLIST ~50% ~50% ~50% 100 models Typical mistakes http://prekopcsak.com Comments? Does the preprocessing work without the label?
Should NOT use the label attribute before validation.
Are the observations (rows in the table) independent?
Should NOT have duplicate rows.
Cross-validation 63% 500 attributes 100 rows random 0-1 labels 100 rows 10 attributes random 0-1 labels 1000 rows 500 attributes 1000 rows 54% 10 attributes Cross-validation 100 models 100 rows 53% ~50% ~50% ~50% ... ~50% 100 models 1000 rows Pick the best 100 models ...