Timeline for Dealing with imbalanced data-set and cross-validation
Current License: CC BY-SA 4.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 20, 2019 at 11:06 | comment | added | kjetil b halvorsen♦ | See this other posts. | |
| Jul 20, 2019 at 11:01 | answer | added | Frank Harrell | timeline score: 2 | |
| Jul 20, 2019 at 3:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Mar 21, 2019 at 12:49 | comment | added | Atheer | You mean by relative frequencies and relative costs, which of the two classes are more significant and the prediction results of it affects more in the real world? | |
| Mar 21, 2019 at 0:45 | comment | added | cbeleites | IMHO the more important question is not whether your data is balanced, but whether the relative frequencies of the classes are what you can expect to meet in the real world of the application? And what the relative costs of the various misclassifications are. | |
| Mar 20, 2019 at 18:25 | comment | added | usεr11852 | You can stratify our sampling if you wish. I do not think it will make a huge difference but if might make it such that you have the same ratio across all folds. | |
| Mar 20, 2019 at 18:01 | comment | added | Atheer | so, no need to modify the cross-validation code above? I thought of separating the data into their classes, and in each fold, I take 90% train,10% test from first class, and from second class as well. Then combine them and randomize them then build the classification model. | |
| Mar 20, 2019 at 16:18 | comment | added | usεr11852 | No it is not consider imbalanced. No, there is little reason to under-sample your malignant class. | |
| Mar 20, 2019 at 13:58 | answer | added | martino | timeline score: 0 | |
| Mar 20, 2019 at 12:30 | review | First posts | |||
| Mar 20, 2019 at 16:18 | |||||
| Mar 20, 2019 at 12:25 | history | asked | Atheer | CC BY-SA 4.0 |