dylanrandle / pynterp

Interpretable Machine Learning in Python

pynterp: Interpretable Machine Learning in Python

The bottleneck to deploying machine learning, and therefore its use more broadly in a great range of problems in business and society, is a lack of interpretability. This is the sentiment "we can't trust this black-box". This repository aims to replicate various interpretable machine learning algorithms for their broad use and dissemination.

Installation

It is highly recommended to use the provided conda environment:

1. conda env create -f environment.yml
2. conda activate pynterp

To install, run:

1. python setup.py install

The basic interface is:

from pynterp.rules.decision_set import DecisionSet
ds = DecisionSet()
ds.fit(X_train, y_train)

Rules-Based Methods

Rules-based methods are models that learn a list or set of IF-THEN-ELSE rules; they are highly interpretable and appealing to humans. The current implementation of DecisionSet follows the paper by Lakkaraju et. al.

TODO

October 23, 2019:

• Implement test suite and continuous integration framework
• Incorporate Bayesian decision lists (due to Rudin et. al)