https://github.com/scikit-garden/scikit-garden
A garden for scikit-learn compatible trees
https://github.com/scikit-garden/scikit-garden
forest machine-learning python scientific-computing scikit-learn-api tree
Last synced: 6 months ago
JSON representation
A garden for scikit-learn compatible trees
- Host: GitHub
- URL: https://github.com/scikit-garden/scikit-garden
- Owner: scikit-garden
- License: other
- Created: 2017-04-19T19:00:21.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-06-20T13:36:50.000Z (11 months ago)
- Last Synced: 2024-11-14T14:47:52.551Z (6 months ago)
- Topics: forest, machine-learning, python, scientific-computing, scikit-learn-api, tree
- Language: Python
- Homepage: http://scikit-garden.github.io/
- Size: 320 KB
- Stars: 285
- Watchers: 12
- Forks: 75
- Open Issues: 53
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Scikit-Garden
[](https://travis-ci.org/scikit-garden/scikit-garden)
[](https://circleci.com/gh/scikit-garden/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 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.
```python
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/