Timeline for Machine Learning with sometimes missing data
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 1, 2016 at 7:02 | comment | added | Neil Slater | Missing a very common approach that can go with the mean/median approach - adding a (typically 0 or 1 valued) column that explicitly records whether the data was originally present. Sometimes absence of the value is a predictive feature by itself. | |
| S Jun 28, 2016 at 21:05 | history | suggested | Soma Holiday | CC BY-SA 3.0 |
spelling correction
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| Jun 28, 2016 at 20:43 | review | Suggested edits | |||
| S Jun 28, 2016 at 21:05 | |||||
| Jun 28, 2016 at 14:38 | comment | added | wabbit | If the idea is to use k nearest neighbors (makes sense when the # data points is not very high) why not directly determine the target using kNN (based on whatever attributes are available)instead of first imputing the missing attributes and then using a regression model? | |
| Jun 27, 2016 at 20:48 | history | answered | Rohan | CC BY-SA 3.0 |