Scikit-Garden

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 depends on NumPy, SciPy, Scikit-Learn and Cython. So make sure these dependencies are installed using pip:

pip3 install setuptools numpy scipy scikit-learn cython

After that 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)

Important links

• API Reference: https://scikit-garden.github.io/api/
• Examples: https://scikit-garden.github.io/examples/