Timeline for How to transform time series data to apply supervised learning algorithms to it?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 8, 2019 at 21:04 | vote | accept | manuel mourato | ||
| Jul 8, 2019 at 20:55 | comment | added | oW_ | why don't you have that data for the test data? you can transform the entire dataset before splitting in training and test data (be careful not to split by client across training and test data if you have multiple datapoints per client) | |
| Jul 8, 2019 at 20:38 | comment | added | manuel mourato | Thank you very much for your answer. Aggregating all previous data points into new features makes a lot of sense, but I have one question: when you said " For example the number of previous (un)successful call attempts", in this case, this feature can only be derived for the training data, in which I know the outcomes for all data points. But for test data, this column would always be empty, and thus would not be meaningfull. Am I correct? | |
| Jul 8, 2019 at 20:29 | history | answered | oW_ | CC BY-SA 4.0 |