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        "description": "# OptGBM\n\n[![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)\n[![codecov](https://codecov.io/gh/Y-oHr-N/OptGBM/branch/master/graph/badge.svg)](https://codecov.io/gh/Y-oHr-N/OptGBM)\n[![PyPI](https://img.shields.io/pypi/v/OptGBM)](https://pypi.org/project/OptGBM/)\n[![PyPI - License](https://img.shields.io/pypi/l/OptGBM)](https://pypi.org/project/OptGBM/)\n[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/Y-oHr-N/OptGBM/master)\n\nOptGBM (= [Optuna](https://optuna.org/) + [LightGBM](http://github.com/microsoft/LightGBM)) provides a scikit-learn compatible estimator that tunes hyperparameters in LightGBM with Optuna.\n\n## Examples\n\n```python\nimport optgbm as lgb\nfrom sklearn.datasets import load_boston\n\nreg = lgb.LGBMRegressor(random_state=0)\nX, y = load_boston(return_X_y=True)\n\nreg.fit(X, y)\n\ny_pred = reg.predict(X, y)\n```\n\nBy default, the following hyperparameters will be searched.\n\n- `bagging_fraction`\n- `bagging_freq`\n- `feature_fractrion`\n- `lambda_l1`\n- `lambda_l2`\n- `max_depth`\n- `min_data_in_leaf`\n- `num_leaves`\n\n## Installation\n\n```\npip install optgbm\n```\n\n## Testing\n\n```\ntox\n```\n\n\n",
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