Documentation and Pypi Released for §0§, a Tool Enhancing Machine Learning Transparency
| Source: Mastodon | Original article
A model-agnostic tool for Machine Learning explainability is now available.
Documentation and a PyPI package are now available for the `teller`, a model-agnostic tool designed to enhance Machine Learning explainability. This tool is significant because it can be applied to various Machine Learning models, as long as they have `fit` and `predict` methods and are used for tabular data.
The `teller` relies on Taylor series to explain model predictions, allowing for the approximation of sensitivities of predictions to changes in explanatory variables. This approach makes it a valuable resource for developers seeking to understand and interpret their Machine Learning models better.
As the `teller` continues to develop, with its current version available on PyPI, it will be interesting to watch how it is adopted and integrated into existing Machine Learning workflows. Its model-agnostic nature and straightforward methodology could make it a widely used tool in the field of Machine Learning explainability.
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