Timeline for Is Gradient Boosting Regression Tree able to learn linear models
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Aug 27, 2018 at 9:06 | comment | added | mkt | (+1) This is interesting - could you tell me what machine learning methods do not require training/testing sets to be from the same population? | |
| Aug 26, 2018 at 6:31 | comment | added | David Dale | The good practice is to use the stacked ensemble. First, a linear model makes predictions - it may be not very accurate, but it is good at extrapolation. Then, the GBDT predict residuals of the linear model, which can increse accuracy dramatically without losing sensitivity to trends. | |
| Aug 26, 2018 at 5:51 | comment | added | Hao Yu | Thanks!One point I can think is that we can use transfer learning if train and test data have differernt distributions, but the train and test sets differ only in scale, I am not be able to find any transfer learning method that can deal with this case. Do you have any suggestion?Thanks | |
| Aug 26, 2018 at 5:35 | history | answered | Matthew Drury | CC BY-SA 4.0 |