https://github.com/krzjoa/scikit-gbm

scikit-learn compatible tools to work with GBM models
https://github.com/krzjoa/scikit-gbm

data-science feature-engineering gbm gradient-boosting machine-learning scikit-learn

Last synced: 11 months ago
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scikit-learn compatible tools to work with GBM models

• Host: GitHub
• URL: https://github.com/krzjoa/scikit-gbm
• Owner: krzjoa
• License: mit
• Created: 2023-04-14T22:43:53.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-08-12T21:37:08.000Z (almost 3 years ago)
• Last Synced: 2025-06-19T21:05:09.483Z (12 months ago)
• Topics: data-science, feature-engineering, gbm, gradient-boosting, machine-learning, scikit-learn
• Language: Python
• Homepage: https://scikit-gbm.readthedocs.io
• Size: 3.28 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# scikit-gbm

[![Documentation Status](https://readthedocs.org/projects/scikit-gbm/badge/?version=latest)](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest)

[![PyPI version](https://badge.fury.io/py/scikit-gbm.svg)](https://badge.fury.io/py/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

Fo the moment, you can find the following tools in the library:

* `GBMFeaturizer`

* `GBMDiscretizer`

* `trees_to_dataframe`

* `AXIL`

Take a look at the [documentation](https://scikit-gbm.readthedocs.io/en/latest/?badge=latest) to learn more.

A simple example, how to use `GBMFeaturizer` in a classification task.

```python

# Classification

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.pipeline import Pipeline

from sklearn.linear_model import LogisticRegression

from skgbm.preprocessing import GBMFeaturizer

from xgboost import XGBClassifier

X, y = make_classification()

X_train, X_test, y_train, y_test = train_test_split(X, y)

pipeline = \

    Pipeline([

        ('gbm_featurizer', GBMFeaturizer(XGBClassifier())),

        ('logistic_regression', LogisticRegression())

    ])

# Try also:

# ('gbm_featurizer', GBMFeaturizer(GradientBoostingClassifier())),

# ('gbm_featurizer', GBMFeaturizer(LGBMClassifier())),

# ('gbm_featurizer', GBMFeaturizer(CatBoostClassifier())),

# Predictions for the test set

pipeline_pred = pipeline.predict(X_test)

```