Timeline for What explains a sudden change in the magnitude of logistic regression coefficients when increasing the sample size
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Aug 13, 2018 at 14:43 | vote | accept | Pieter | ||
| Aug 11, 2018 at 12:34 | comment | added | EdM |
@Pieter that’s the rule of thumb for completely unpenalized regression. Penalization can be thought of as reducing the number of effective predictors so that you can work with fewer cases. Don’t know how to gauge the amount of penalization introduced by your C=1000 parameter setting, but that might explain why things stabilized at lower sample sizes than might be expected absent penalization.
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| Aug 11, 2018 at 8:32 | comment | added | Pieter | The labels are indeed distributed by a 50/50 split. The number of features however is 100 and not 10, would this suggest that I need 3000 samples? | |
| Aug 10, 2018 at 19:00 | history | answered | EdM | CC BY-SA 4.0 |