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Welcome to the teller's website.

There is an increasing need for transparency and fairness in Machine Learning (ML) models predictions. Consider for example a banker who has to explain to a client why his/her loan application is rejected, or a health professional who must explain what constitutes his/her diagnosis. Some ML models are indeed very accurate, but are considered to be hard to explain, relatively to popular linear models.

Source of figure: James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013. Source: James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: springer, 2013.

We do not want to sacrifice this high accuracy to explainability. Hence: ML explainability. There are a lot of ML explainability tools out there, in the wild (don't take my word for it).

The teller is a model-agnostic tool for ML explainability - agnostic, as long as this model possesses methods fit and predict. The teller's philosophy is to rely on Taylor series to explain ML models predictions: a little increase in model's explanatory variables + a little decrease, and we can obtain approximate sensitivities of its predictions to changes in these explanatory variables.

The teller's source code is available on GitHub, and you can read posts about it in this blog.

Looking for a specific function? You can also use the search function available in the navigation bar.

Installing

  • From Pypi, stable version:
pip install the-teller
  • From Github, for the development version:
pip install git+https://github.com/Techtonique/teller.git

Quickstart

Documentation

Contributing

Want to contribute to teller's development on Github, read this!