Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The initial focus on the library is on black-box, instance based model explanations.
Goals¶
Provide high quality reference implementations of black-box ML model explanation algorithms
Define a consistent API for interpretable ML methods
Support multiple use cases (e.g. tabular, text and image data classification, regression)
Implement the latest model explanation, concept drift, algorithmic bias detection and other ML model monitoring and interpretation methods
Overview
Methods
Examples
- Anchor explanations for income prediction
- Anchor explanations on the Iris dataset
- Anchor explanations for movie sentiment
- Anchor explanations for ImageNet
- Anchor explanations for fashion MNIST
- Contrastive Explanations Method (CEM) applied to MNIST
- Contrastive Explanations Method (CEM) applied to Iris dataset
- Counterfactual instances on MNIST
- Counterfactuals guided by prototypes on MNIST
- Counterfactuals guided by prototypes on Boston housing dataset
- Counterfactual explanations with one-hot encoded categorical variables
- Counterfactual explanations with ordinally encoded categorical variables
- Linearity measure applied to Iris
- Linearity measure applied to fashion MNIST
- Trust Scores applied to Iris
- Trust Scores applied to MNIST
API reference