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I want to use additional data to 'update' an already trained Light Gradient Boosting Model (LGBM). Is there a way to do that?

I am looking for an approach that uses the Skealrn API and thus can be used in a pipeline.

desertnaut
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Emil Filipov
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  • Have you done any research before asking? [LightGBM: continue training a model](https://stackoverflow.com/questions/45654998/lightgbm-continue-training-a-model), [How does retrain work?](https://github.com/Microsoft/LightGBM/issues/1469) - https://idownvotedbecau.se/noresearch/ – desertnaut May 28 '20 at 16:46
  • @desertnaut Would that work with the Sklearn API? I also want to use the model in a python Pipeline. I am not sure if those approaches are 'friendly' to what i want to do. – Emil Filipov May 28 '20 at 17:04
  • I have no idea, neither did you mention anything about this in your question (which is arguably the place to do so, and not in the comments). I didn't provide an answer, just a comment. – desertnaut May 28 '20 at 17:07

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An LGBM model in python can be fitted both with the original model API and with the Sklearn API. I couldn't find any examples of using the Sklearn API for continuous learning. Regardless of that, you can fit a model either way and it is compatible with the .train() function from the original API. It can be saved with save_model() or with joblib.dump().

This does not affect its compatibility with Python Pileline() - it is perfectly compatible.

Emil Filipov
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