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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.


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.


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.


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

  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)},