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[python-package] How to refit a classifier? #6461
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Thanks for using LightGBM. Is it absolutely necessary to "refit" (modify the values of the leaf nodes without changing the total number of trees)? Or would it bee acceptable to add more trees, trained on the newly-arrived data? If you clarify that precisely, it would help us to offer some advice. Please also see this explanation: https://stackoverflow.com/questions/73664093/lightgbm-train-vs-update-vs-refit/73669068#73669068 |
Also...I see that you double-posted this here and on Stack Overflow (link). Please do not do that. Maintainers here also monitor the |
Hi James, thanks for the response. It would be best if we can use the same exact "refit", if not I can use the update function? also, I literally posted on StackOverflow about 30 min ago. I waited few days, in-case this got backlog and would take longer than expected to get a respond. Out of respect to you I will link this to StackOverflow. Thanks in advance. |
What is preventing you from using We would be happy to help but need your help to understand what specifically you are looking for. |
I have a timeseries data that I have to fit a classifier, I would like to re-train it every month with new data coming in. I would like to keep some consistency, so I prefer to pre_warm the tree with the previous boosting trees and refit the data, or just iterate few times to get the results I need. In other words I want similar tree structure more or less.
Seems like the refit function is the best way to do it but unfortunately It doesnt seem to be an option for scikit API only boosters. What is the best way I can approach this? I have done the following thus far:
I'm sure this is wrong by just looking at the outputs can anyone point to the right direction? Thanks
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