lightgbm

Summary

mypy shows some issues in LightGBM's Python package.

mypy \
    --exclude='python-package/compile/|python-package/build' \
    --ignore-missing-imports \
    python-package/

python-package/lightgbm/compat.py:12: error: Name 'Series' already defined (possibly by an import)
python-package

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

Is your feature request related to a problem? Please describe.
Implements classification_report for classification metrics.(https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)

Our xgboost models use the binary:logistic' objective function, however the m2cgen converted version of the models return raw scores instead of the transformed scores.

This is fine as long as the user knows this is happening! I didn't, so it took a while to figure out what was going on. I'm wondering if perhaps a useful warning could be raised for users to alert them of this issue? A warning

When using r2 as eval metric for regression task (with 'Explain' mode) the metric values reported in Leaderboard (at README.md file) are multiplied by -1.
For instance, the metric value for some model shown in the Leaderboard is -0.41, while when clicking the model name leads to the detailed results page - and there the value of r2 is 0.41.
I've noticed that when one of R2 metric values in the L

Currently we don't test (or document) that Eland should work with data streams, we should probably test that everything works properly.

Does HyperGBM's make_experiment return the best model?
How does it work on paramter tuning? It's say that, what's its seach space (e.g. in XGboost)???