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.
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.
We built a Python library to generate Contribution-Value plots for any
scikit-learn model, and is optimized for usage in interactive environments such as Jupyter Notebooks.
If you want to refer to our visualization, please cite our paper using the following BibTeX entry: