Scikit-Garden

Forked from https://github.com/scikit-garden/scikit-garden

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

Ordered prediction intervals on the Boston dataset.

Installation

This Scikit-Garden fork can be installed using pip.

pip install git+https://git@github.com/Demangio/scikit-garden.git
or
pip install scikit-garden-forked

Available models

Regressors

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

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 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)

What changes in this release

Change default predict method to the same as QuantReg package. This version is faster and include parametric estimation.
Adaptation of code to higher versions of dependencies.

Important links

• API Reference: https://scikit-garden.github.io/api/
• Examples: https://scikit-garden.github.io/examples/
• Modifications source: https://stackoverflow.com/questions/51483951/quantile-random-forests-from-scikit-garden-very-slow-at-making-predictions