Important variables determined through data-based variable importance methods
Project description
PermutationImportance
Welcome to the PermutationImportance library!
PermutationImportance is a Python package for Python 2.7 and 3.6+ which provides several methods for computing data-based predictor importance. The methods implemented are model-agnostic and can be used for any machine learning model in many stages of development. The complete documentation can be found at our Read The Docs.
Version History
- 1.2.1.8: Shuffled pandas dataframes now retain the proper row indexing
- 1.2.1.7: Fixed a bug where pandas dataframes were being unshuffled when concatenated
- 1.2.1.5: Added documentation and examples and ensured compatibility with Python 3.5+
- 1.2.1.4: Original scores are now also bootstrapped to match the other results
- 1.2.1.3: Corrected an issue with multithreading deadlock when returned scores were too large
- 1.2.1.1: Provided object to assist in constructing scoring strategies
- Also added two new strategies with bootstrapping support
- 1.2.1.0: Metrics can now accept kwargs and support bootstrapping
- 1.2.0.0: Added support for Sequential Selection and completely revised backend
for proper abstraction and extension
- Return object now keeps track of
(context, result)
pairs abstract_variable_importance
enables implementation of custom variable importance methods- Backend is now correctly multithreaded (when specified) and is OS-independent
- Return object now keeps track of
- 1.1.0.0: Revised return object of Permutation Importance to support easy retrieval of Breiman- and Lakshmanan-style importances
- 1.0.0.0: Published with
pip
support!
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