SLISEMAP: Supervised dimensionality reduction through local explanations
This addresses the challenge of interpreting complex black box models for researchers and practitioners in machine learning, though it is incremental as it builds on existing local explanation and dimensionality reduction methods.
The authors tackled the problem of creating global visualizations for black box models with high fidelity by proposing SLISEMAP, a supervised manifold visualization method that simultaneously finds local explanations for all data items and projects them in a two-dimensional space based on similarity, resulting in embeddings with consistent local white box models as demonstrated in comparisons.
Existing methods for explaining black box learning models often focus on building local explanations of model behaviour for a particular data item. It is possible to create global explanations for all data items, but these explanations generally have low fidelity for complex black box models. We propose a new supervised manifold visualisation method, SLISEMAP, that simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. We provide a mathematical derivation of our problem and an open source implementation implemented using the GPU-optimised PyTorch library. We compare SLISEMAP to multiple popular dimensionality reduction methods and find that SLISEMAP is able to utilise labelled data to create embeddings with consistent local white box models. We also compare SLISEMAP to other model-agnostic local explanation methods and show that SLISEMAP provides comparable explanations and that the visualisations can give a broader understanding of black box regression and classification models.