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Visual Exploration of
Machine Learning Explanations

ExplainExplore is a new approach for analyzing and understanding classification models
using state of the art machine learning explanation techniques.

Explain

ExplainWhy?

Modern machine learning models are usually applied in a black-box manner: only the input (data) and
output (predictions) are considered, the inner workings are considered too complex to understand.

ExplainHow?

To explain a complex machine learning model, we generate a local approximation (or surrogate model) that can be easily explained.

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We start off with the data set we would like to generate explanations for, and pick one of the instances. The background represents the class probabilities output by the complex ML model.

Explore

ExploreWhy?

Unfortunately, explanations techniques are not so straightforward. Many different explanations can be generated which
may all be equally valid and useful. Determining which is best remains challenging due to the subjective nature of interpretability.

ExploreHow?

We built ExplainExplore: a new approach for analyzing and understanding classification models
using state of the art machine learning explanation techniques. The system offers three perspectives:

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Local

Individual instances are explained using feature contribution vectors extracted from surrogate models. This shows how much every feature contributed to the prediction.

To assert the correctness of these values, information on prediction certainty (P) and surrogate fit (R2) are shown.

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Context

In the context view, nearby data points are shown as well as the class probability of complex reference model (global) and surrogate (local). The expert can use this to assert whether the surrogate is locally faithful to the reference model, and explore the effect of instance perturbations.

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Global

The global overview combines the contribution vectors for all data in one Parallel Coordinate Plot (PCP). This helps the data scientist to identify whether the seleted instance was classified similar to other instances, and whether the model has 'strategies' (clusters in the contribution vectors).

Try it out!

ExplainExplore was built to support a wide variety of datasets and models. The system supports:


We created a limited version of ExplainExplore that runs completely in the browser (as opposed to
requiring a Python backend). Try it out below.

Run the demo

Citation

If you want to refer to our system, please cite our paper using the following BibTeX entry:

@inproceedings{collaris2020explainexplore,
  title={ExplainExplore: Visual Exploration of Machine Learning Explanations},
  author={Collaris, Dennis and van Wijk, Jarke J},
  booktitle={2020 IEEE Pacific Visualization Symposium (PacificVis)},
  pages={26--35},
  year={2020},
  organization={IEEE}
}
Paper