Scikit-garden or skgarden (pronounced as skarden) is a garden for scikit-learn compatible trees.


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

pip install setuptools numpy scipy scikit-learn cython

After that Scikit-Garden can be installed using pip.

pip install scikit-garden


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
boston = load_boston()
X, y =,

### Use MondrianForests for variance estimation
from skgarden import MondrianForestRegressor
mfr = MondrianForestRegressor(), 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), y)
y_mean = rfqr.predict(X)
y_median = rfqr.predict(X, 50)