Metadata-Version: 2.1
Name: OptGBM
Version: 0.9.0
Summary: Optuna + LightGBM \= OptGBM
Home-page: UNKNOWN
Author: Kon
License: MIT License
Description: # OptGBM
        
        [![Python package](https://github.com/Y-oHr-N/OptGBM/workflows/Python%20package/badge.svg?branch=master)](https://github.com/Y-oHr-N/OptGBM/actions?query=workflow%3A%22Python+package%22)
        [![codecov](https://codecov.io/gh/Y-oHr-N/OptGBM/branch/master/graph/badge.svg)](https://codecov.io/gh/Y-oHr-N/OptGBM)
        [![PyPI](https://img.shields.io/pypi/v/OptGBM)](https://pypi.org/project/OptGBM/)
        [![PyPI - License](https://img.shields.io/pypi/l/OptGBM)](https://pypi.org/project/OptGBM/)
        [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Y-oHr-N/OptGBM/master)
        
        OptGBM (= [Optuna](https://optuna.org/) + [LightGBM](http://github.com/microsoft/LightGBM)) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.
        
        ## Examples
        
        ```python
        import optgbm as lgb
        from sklearn.datasets import load_boston
        
        reg = lgb.LGBMRegressor(random_state=0)
        X, y = load_boston(return_X_y=True)
        
        reg.fit(X, y)
        
        y_pred = reg.predict(X, y)
        ```
        
        By default, the following hyperparameters will be searched.
        
        - `bagging_fraction`
        - `bagging_freq`
        - `feature_fractrion`
        - `lambda_l1`
        - `lambda_l2`
        - `max_depth`
        - `min_data_in_leaf`
        - `num_leaves`
        
        ## Installation
        
        ```
        pip install optgbm
        ```
        
        ## Testing
        
        ```
        tox
        ```
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: testing
