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Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

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CircleCIJoin the chat at https://gitter.im/FINRAOS/model-validation-toolkitPyPI version

Model Validation Tookit

Installation

Run pip install mvtk.

Windows users: Until Jaxlib is supported on windows natively you will need to either use this library from a Linux subsystem or within a Docker container. Alternatively, you can build jaxlib from source.

Developers

Check out this repository and cd into the directory.

Run pip install -e ".[doc]".

The [doc] is used to install dependencies for building documentation. You will need pandoc installed.

Submodules

You can import:

  • mvtk.credibility for assessing credibility from sample size.
  • mvtk.interprenet for building interpretable neural nets.
  • mvtk.thresholding for adaptive thresholding.
  • mvtk.sobol for Sobol sensitivity analysis
  • mvtk.supervisor for divergence analysis
  • mvtk.metrics for specialised metrics
  • mvtk.bias_variance for bias-variance decomposition

Documentation

You can run make -C docs html on a Mac or make.bat -C docs html on a PC to just rebuild the docs. In this case, point your browser to docs/_build/html/index.html to view the homepage. If your browser was already pointing to documentation that you changed, you can refresh the page to see the changes.

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Model Validation Toolkit is a collection of tools to assist with validating machine learning models prior to deploying them to production and monitoring them after deployment to production.

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