Forest algorithms are powerful ensemble methods for classification and regression. However, predictions from these algorithms do contain some amount of error.

forest-confidence-interval is a Python module that adds a calculation of variance and computes confidence intervals to the basic functionality implemented in scikit-learn random forest regression or classification objects. The core functions here calculate an in-bag and error bars for random forest objects

Compatible with Python2.7 and Python3.6

This module is based on R code from Stefan Wager (see important links below) and is licensed under the MIT open source license (see LICENSE)

scikit-learn - http://scikit-learn.org/

Stefan Wager's randomForestCI - https://github.com/swager/randomForestCI

Before installing the module you will need numpy, scipy and scikit-learn.
Dependencies associated with the previous modules may need root privileges to install

pip install numpy scipy scikit-learn

can also install dependencies with:

 pip install -r requirements.txt

To install forest-confidence-interval execute:

pip install forestci

or, if you are installing from the source code:

python setup.py install

If would like to install the development version of the software use:

pip install git+git://github.com/scikit-learn-contrib/forest-confidence-interval.git

See examples gallery

Contributions are very welcome, but we ask that contributors abide by the [contributor covenant)[http://contributor-covenant.org/version/1/4/].

To report issues with the software, please post to the issue log Bug reports are also appreciated, please add them to the issue log after verifying that the issue does not already exist. Comments on existing issues are also welcome.

Please submit improvements as pull requests against the repo after verifying that the existing tests pass and any new code is well covered by unit tests. Please write code that complies with the Python style guide, PEP8

Requires installation of nose package. Tests are located in the forestci/tests folder and can be run with the nosetests command in the main directory