Timeline for Machine Learning with sometimes missing data
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 30, 2016 at 20:02 | answer | added | Emre | timeline score: 2 | |
| Jun 30, 2016 at 19:58 | comment | added | c4b4d4 | @Emre It is "random", when the iPhone sends probe requests and is near all Wi-Fi routers then I get all the data, but the iPhone is only sending probe requests when you are looling for Wi-Fis within the Settings of the phone or once every random time when you are not connected to Wi-Fi or when the phone is "sleeping". So I suppose it is random. | |
| Jun 30, 2016 at 19:56 | comment | added | Emre | You can do that. What fraction of your data is missing; is it random, or does it follow a pattern? It would help to update your question with relevant details. | |
| Jun 30, 2016 at 19:37 | comment | added | c4b4d4 | @Emre What if I build different models with just the data I have? So I have one model for when I have 3 parameters only, then another model for when I have 6 parameters, etc. | |
| Jun 30, 2016 at 19:12 | answer | added | BitsInForce | timeline score: 0 | |
| Jun 28, 2016 at 11:52 | comment | added | DaL | See en.wikipedia.org/wiki/Imputation_(statistics) | |
| Jun 28, 2016 at 10:22 | comment | added | Erhard Dinhobl | Depending on your usage but replacing with a mean value would be a good approach as a first step! | |
| Jun 27, 2016 at 21:47 | comment | added | Emre | The ideal solution would be to have a probability distribution over the parameters and marginalize the missing ones. | |
| Jun 27, 2016 at 20:48 | answer | added | Rohan | timeline score: 4 | |
| Jun 27, 2016 at 19:10 | history | edited | c4b4d4 | CC BY-SA 3.0 |
added 6 characters in body
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| Jun 27, 2016 at 18:28 | history | asked | c4b4d4 | CC BY-SA 3.0 |