Interpretability Suite

A comprehensive collection of Machine Learning interpretability methods, offering users a reference to select the best-suited method for their needs, with a focus on providing insights into ML model predictions through organisation, a common Python interface, and a user interface.

How is it unique?

Interpretability Suite stands out by organizing various ML interpretability methods, both from the van der Schaar lab and third-party sources, into tables and flow-diagrams based on their use-case and explanation. It supports tabular datasets, time-series data, unsupervised model explainers, and individualised treatment effect explainers, providing both a Python interface and a visual user interface for seamless implementation and analysis.

How is it useful?

Interpretability Suite can, for instance:

1. Enhance transparency and trust in ML models by providing clear explanations for model predictions, allowing stakeholders to better understand the reasoning behind critical decisions.

2. Improve collaboration between teams by offering a visual user interface that enables non-technical team members to view and analyse explanations without Python knowledge.

3. Accelerate the implementation of interpretability methods by providing a standardised Python interface, streamlining the integration of explainers into existing projects.

4. Foster informed decision-making by guiding users to the most appropriate interpretability methods based on their specific use-case and data type.

With Interpretability Suite, users and teams can ensure that ML models are more transparent, understandable, and trustworthy, fostering collaboration and informed decision-making across the industry.