Timeline for To calculate my confusion matrix with recall and precision, my test set need to be equal(balanced)?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 21, 2019 at 17:24 | vote | accept | 0nroth1 | ||
| Nov 21, 2019 at 16:50 | comment | added | Yohanes Alfredo | I edited my answer and provided a link. You can check it. It is quite related. Basically AFAIK, there is no method that normalize everything, without reducing information. Especially since your dataset is small, imo there is no need to simplify the confusion matrix. You can include other metric and since your model has many false positive you need to include recall/f1-score on your report | |
| Nov 21, 2019 at 16:41 | history | edited | Yohanes Alfredo | CC BY-SA 4.0 |
added 165 characters in body
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| Nov 21, 2019 at 16:38 | comment | added | 0nroth1 | I want to know what is the best approach. If is equalize or not | |
| Nov 21, 2019 at 16:37 | comment | added | 0nroth1 | Because I need to present this results in my university | |
| Nov 21, 2019 at 14:30 | history | answered | Yohanes Alfredo | CC BY-SA 4.0 |