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scikit-gbm\n\n[![Documentation Status](https://readthedocs.org/projects/scikit-gbm/badge/?version=latest)](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest)\n[![PyPI version](https://badge.fury.io/py/scikit-gbm.svg)](https://badge.fury.io/py/scikit-gbm)\n\nscikit-learn compatible tools to work with GBM models\n\n## Installation\n\n```\npip install scikit-gbm\n\n# or \n\npip install git+https://github.com/krzjoa/scikit-gbm.git\n```\n\n## Usage\n\nFo the moment, you can find the following tools in the library:\n\n* `GBMFeaturizer`\n* `GBMDiscretizer`\n* `trees_to_dataframe`\n* `AXIL`\n\nTake a look at the [documentation](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest) to learn more.\nA simple example, how to use `GBMFeaturizer` in a classification task.\n\n```python\n\n# Classification\nfrom sklearn.datasets import make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.linear_model import LogisticRegression\n\nfrom skgbm.preprocessing import GBMFeaturizer\nfrom xgboost import XGBClassifier\n\nX, y = make_classification()\nX_train, X_test, y_train, y_test = train_test_split(X, y)\n\npipeline = \\\n    Pipeline([\n        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),\n        ('logistic_regression', LogisticRegression())\n    ])\n\n# Try also:\n# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),\n# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),\n# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),\n\n# Predictions for the test set\npipeline_pred = pipeline.predict(X_test)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrzjoa%2Fscikit-gbm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkrzjoa%2Fscikit-gbm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkrzjoa%2Fscikit-gbm/lists"}