Timeline for Merging sparse and dense data in machine learning to improve the performance
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 18, 2016 at 17:55 | comment | added | Sagar Waghmode | As I said already, I have generated 1k principal components which were explaining 0.97 variance. | |
| Apr 18, 2016 at 15:22 | comment | added | Tagar | Interesting. Thanks for sharing. We have very similar dataset to yours (1k-2k sparse features). Just out of curiosity, how many principal componenets you have generated? If that number is too low, this may explain why AUC went down. | |
| Apr 18, 2016 at 10:17 | comment | added | Sagar Waghmode | Wow, this has actually brought down AUC :( Not sure, what it means, need to check the feature importance and all. But my philosophy is, out of around 2.3k sparse features, I used 1k features which were explaining 0.97 variance ratio, this loss of information may have brought down AUC. | |
| Apr 18, 2016 at 6:15 | review | First posts | |||
| Apr 18, 2016 at 7:07 | |||||
| Apr 18, 2016 at 6:11 | history | answered | Tagar | CC BY-SA 3.0 |