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dmlc / xgboost

Star 22k
Open

Better support for mypy.

1
trivialfis
trivialfis commented Dec 13, 2020

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.

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feature-request good first issue
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LightGBM

microsoft / LightGBM

Star 13.3k
Open

[ci] warnings from mypy

10
jameslamb
jameslamb commented Jan 27, 2021

Summary

mypy shows some issues in LightGBM's Python package.

mypy \
    --exclude='python-package/compile/|python-package/build' \
    --ignore-missing-imports \
    python-package/
18 errors in 4 files (click me) 
python-package/lightgbm/compat.py:12: error: Name 'Series' already defined (possibly by an import)
python-package
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help wanted good first issue
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catboost / catboost

Star 6.3k
Open

Extend Callback Interface

1
ayulockin
ayulockin commented Dec 1, 2021

I am working on creating a WandbCallback for Weights and Biases. I am glad that CatBoost has a callback system in place but it would be great if we can extend the interface.

The current callback only supports after_iteration that takes info. Taking inspiration from XGBoost callback system it would be great if we can have before iteration that takes info, before_training, and `after

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h2oai / h2o-3

Star 5.7k

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

python java data-science machine-learning multi-threading opensource r big-data spark deep-learning hadoop random-forest gpu naive-bayes h2o distributed pca gbm ensemble-learning automl
  • Updated Dec 17, 2021
  • Jupyter Notebook

serengil / chefboost

Star 287

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

python data-science machine-learning data-mining random-forest kaggle id3 gbdt gbm gbrt gradient-boosting-machine cart adaboost decision-trees gradient-boosting c45-trees categorical-features gradient-boosting-machines regression-tree
  • Updated Dec 5, 2021
  • Python

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