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Scikit-Garden

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Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.

Weights at different depths of a MondrianTree

Ordered prediction intervals on the Boston dataset.

Installation

Scikit-Garden can be installed using pip.

pip install scikit-garden

Available models

Regressors

  • MondrianForestRegressor
  • ExtraTreesRegressor (with return_std support)
  • ExtraTreesQuantileRegressor
  • RandomForestRegressor (with return_std support)
  • RandomForestQuantileRegressor

Classifiers

  • MondrianForestClassifier

Usage

The estimators in Scikit-Garden are Scikit-Learn compatible and can serve as a drop-in replacement for Scikit-Learn's trees and forests.

from sklearn.datasets import load_boston
X, y = load_boston()

### Use MondrianForests for variance estimation
from skgarden import MondrianForestRegressor
mfr = MondrianForestRegressor()
mfr.fit(X, y)
y_mean, y_std = mfr.predict(X, return_std=True)

### Use QuantileForests for quantile estimation
from skgarden import RandomForestQuantileRegressor
rfqr = RandomForestQuantileRegressor(random_state=0)
rfqr.fit(X, y)
y_mean = rfqr.predict(X)
y_median = rfqr.predict(X, 50)

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  • Python 61.9%
  • Cython 37.0%
  • Shell 1.1%