Open jinyyy666 opened 2 years ago
One way I can think of is to just write a wrapper and mock all the functions currently existing for the lightgbm.Booster
class.
@jinyyy666 Thanks for using LightGBM. Unfortunately, we don't have an official approach to merge multiple LightGBM models.
If the goal is just to combine the prediction results of multiple boosting model, I think it can be achieved by simply adding the prediction results from different models together.
However, if the goal is the blending different base learners (trees or linear models) during ONE boosting process, I think it can hardly be achieved by existing GBDT tools without modifying the source code.
If you have any further problems, please feel free to post them here. Thanks!
I have an application that trains a boosting sequence with tree models and linear models like: linear-model, trees, linear-model, and trees. In the end, I want to combine all the tree models into one.
I do not find the function to do that in lightgbm.
In catboost library, they have a function called: sum_models: https://catboost.ai/en/docs/concepts/python-reference_sum_models
which does this.