Timeline for Sklearn Learning Curve Example
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 8, 2017 at 18:23 | comment | added | sandyp | Appreciate it. Care to give some thoughts on one more question that I have been struggling with: stats.stackexchange.com/questions/283877/… | |
| Jun 8, 2017 at 18:15 | comment | added | cbeleites | Other model: Sure, that's another approach (similar to choosing a more restrictive γ). But then, model comparisons also need lots of cases. And, how to restrict your model to get rid of overfitting cannot be seen from the learning curve you discuss here. But in practice I think the important point is to relate observerd generalization error and overfitting to the application needs. | |
| Jun 7, 2017 at 17:26 | comment | added | sandyp | So to summarize your answer, we keep adding training samples as long as the CV error is lower than the training error? My first reaction to reduce over-fitting would be to choose another model. | |
| Jun 7, 2017 at 8:45 | history | answered | cbeleites | CC BY-SA 3.0 |