SLISEMAP: Combining Supervised Dimensionality Reduction with Local Explanations

• Published in: Machine Learning and Knowledge Discovery in Databases pp. 612 - 616
• Main Authors: Björklund, Anton, Mäkelä, Jarmo, Puolamäki, Kai
• Format: Book Chapter
• Language: English
• Published: Cham Springer Nature Switzerland 2023
• Series: Lecture Notes in Computer Science
• Subjects: Explainable AI; Manifold visualisation
• ISBN: 3031264215; 9783031264214
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-26422-1_41

We introduce a Python library, called slisemap, that contains a supervised dimensionality reduction method that can be used for global explanation of black box regression or classification models. slisemap takes a data matrix and predictions from a black box model as input, and outputs a (typically) two-dimensional embedding, such that the black box model can be approximated, to a good fidelity, by the same interpretable white box model for points with similar embeddings. The library includes basic visualisation tools and extensive documentation, making it easy to get started and obtain useful insights. The slisemap library is published on GitHub and PyPI under an open source license.