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Machine Learning Interpretability
through Contribution-Value Plots

Contribution-Value plots are a new visual encoding for interpreting machine learning models.
It can be used as one of the elementary building blocks for interpretability visualization.

Why?

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.

How?

To solve the black-box problem, one key approach is to show the impact of a feature on the model prediction.
These techniques are commonly used as elementary building blocks for interpretability visualization.

We experimented with combining contribution and sensitivity analysis techniques:
our contributions are highighted in blue.

Contribution-Value Plots

A basic example of the Wine Quality data set, and a Random Forest with 100 trees as the complex model we would like to analyze.


Insights about alcohol

  • A more important feature than pH (more vertical dispersion).
  • For some instances a positive, and others a negative contribution.

Insights about pH

  • 2 different values are important: 3.05 and 3.35.
  • There are two clusters that each have a different threshold (use selection).
  • The clusters correspond with high and low values of alcohol respectively.

Citation

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

@inproceedings{collaris2020machine,
  title={Machine Learning Interpretability through Contribution-Value Plots},
  author={Collaris, Dennis and van Wijk, Jarke J},
  booktitle={Proceedings of the 13th International Symposium on Visual Information Communication and Interaction (VINCI 2020)},
  pages={1--5},
  year={2020}
}
Paper