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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.
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).
teller is a model-agnostic tool for ML explainability - agnostic, as long as this model possesses methods
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.
Looking for a specific function? You can also use the search function available in the navigation bar.
- From Pypi, stable version:
pip install the-teller
- From Github, for the development version:
pip install git+https://github.com/Techtonique/teller.git
Want to contribute to teller's development on Github, read this!