Metadata-Version: 2.1
Name: scikit-gbm
Version: 0.0.1
Summary: scikit-learn compatible tools to work with GBM models
Author-email: Krzysztof Joachimiak <joachimiak.krzysztof@gmail.com>
Project-URL: Homepage, https://github.com/krzjoa/scikit-gbm
Project-URL: Bug Tracker, https://github.com/krzjoa/scikit-gbm/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# scikit-gbm
scikit-learn compatible tools to work with GBM models

## Installation

```
pip install scikit-gbm

# or 

pip install git+https://github.com/krzjoa/scikit-gbm.git

```

## Usage

For the moment, the only available class is `GBMFeaturezier`. It's a wrapper around
scikit-learn GBMs, XGBoost, LightGBM and CatBoost models.

```python


# Classification
from sklearn.datasets import make_classification
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression

from skgbm.preprocessing import GBMFeaturizer
from xgboost import XGBClassifier

X, y = make_classification()
# train_test_split

pipeline = \
    Pipeline([
        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),
        ('logistic_regression', LogisticRegression())
    ])

# Try also:
# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),
# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),
# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),


# Regression


```
