Timeline for Overfitting when dealing with nearly the same number of features and observations
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot | replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/ | |
| Jun 16, 2016 at 12:18 | comment | added | Firebug | No, you misunderstood. You use cross-validation to obtain an estimate of the generalized performance of your model, and there's bias and variance in that estimate. Diminishing both would be the ideal. The variance itself is no indicator of overfitting, it just makes the estimation worse. Now, the optimistic bias leads to overfit because your model building strategy is done through hyperparameter optimization based on performance estimates. | |
| Jun 16, 2016 at 8:00 | comment | added | maia | First ,thanks for answering all my question,while using k-fold cross-validation how much variance is good indicator that i overcome overfitting ? i mean as i understand , if all 10 repeating of taring-and-evaluating the classifier in k-fold give us nearly same performance , this indicate that we overcome the overfitting issue which otherwise without using k-fold was main drawback of dealing with same number of feature and observation? | |
| Jun 12, 2016 at 17:03 | history | edited | Firebug | CC BY-SA 3.0 | added 181 characters in body |
| Jun 12, 2016 at 16:34 | history | answered | Firebug | CC BY-SA 3.0 |