Timeline for Server log analysis using machine learning
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 29, 2016 at 14:37 | comment | added | Ashish Tyagi | Is your problem got resolved to predict which user is more likely to cause the next exception and at which feature(and bunch of other stuff to keep track and improve optimization of the application) ? If yes then can you please share your solution approach or anyone else can share ? | |
| Dec 1, 2015 at 7:33 | answer | added | Oliver | timeline score: 12 | |
| Nov 29, 2015 at 11:10 | comment | added | Has QUIT--Anony-Mousse | I think you are overshooting in the objectives. Don't treat ML as a black box to do magic. You have to ask the right questions (and have adequate data for that) to get any result. | |
| Nov 28, 2015 at 21:31 | comment | added | user13684 | If you did have predictive capability to believe a certain user had a high likelihood of an exception what would you do? The goal is optimize the application. Are you trying to refine which bugs engineers should spend their time on instead of just fixing known bugs in the application? Feature engineering may be very important to this task. Also, you may want to consider logistic regression which will produce a 0..1 value which may be interpreted as a probability. | |
| Nov 27, 2015 at 18:14 | review | First posts | |||
| Nov 28, 2015 at 1:23 | |||||
| Nov 27, 2015 at 18:11 | history | asked | elric | CC BY-SA 3.0 |