Timeline for Why data shuffling has such a dramatic effect in K-Neighbours regression?
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 19, 2020 at 17:37 | vote | accept | Santi Peñate-Vera | ||
| Dec 16, 2016 at 13:20 | comment | added | Santi Peñate-Vera | Imagine you have 4 weeks data in hourly steps. To test the method you pick 3 weeks to train and the last week to forecast. If you shuffle the 4 weeks data into train and test sets, you'll have data from the fourth week in the train set, hence hours from the 4th week are used to predict other hours from the fourth week having those hours a great similarity. By not shuffling we won't have any info about how the hours in the fourth week look like. | |
| Dec 16, 2016 at 12:05 | comment | added | einar | Plus also, you could look into the block bootstrap and its variants if you want to do resampling/data perturbation/"shuffling" in a time series setting | |
| Dec 16, 2016 at 12:03 | comment | added | einar | I'm not sure I 100% understand your answer, is it that in the "non-shuffling" case you're trying to predict the future, but when doing shuffling you're just trying to fill in missing points in the past? | |
| Dec 16, 2016 at 11:34 | history | answered | Santi Peñate-Vera | CC BY-SA 3.0 |