Ilya Chernov

>you can use larger `min_data_per_leaf` or `min_hessian_per_leaf` Sure, I tried a few values. If I increase these too much though it results in the model being under-fitted. I use `"regression"`...

>Did it only happen in large-scale data? Although I didn't conduct the same experiment with a smaller fraction of my dataset, I tried bagging fraction before (perhaps with different set...

>Did you use missing value handling? By default, it will enable if feature values contain NaN. you can also try `use_missing=false`. Yes, I do use missing value handling. Will try...

I tried running the same script as before (so without setting `min_data_in_leaf`, `min_sum_hessian_in_leaf`) with the only change being the addition of `"use_missing": False,` to the model's params. And the same...

@guolinke , nope, all features are numerical.

I'll start an internal discussion about this, but I doubt any particular data or even a piece of it will be shared. In the meantime I ran a few other...

Interestingly, the error still happens even with `"max_depth"` and `"num_leaves"` both being set to zero. Perhaps it occurs during some preliminary data verification?

Hello @shiyu1994 , appreciate your rapid response! Do I understand it correctly that the only way to try this version is to do this: https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html#linux ? And if so, will...

I tried a few different ways to install this version in a new conda env. Alas, none of them worked. For instance, `pip install git+git://github.com/shiyu1994/LightGBM@fix-3679` results in: ``` FileNotFoundError: [Errno...

While I'd be able to test this locally, it'll only make sense to run the experiment on a remote machine that has enough processing power. And I'm unable to install...