SelfExplainML/PiML-Toolbox
A Python toolbox for building, validating, and diagnosing interpretable machine learning models through low-code interfaces.
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PiML provides an integrated Python environment for interpretable machine learning, supporting model development and validation workflows. It offers low-code APIs and interactive tools for training interpretable models, analyzing model behavior, and assessing model robustness and reliability. The toolbox wraps various model types including boosted trees, neural networks, and statistical models, and includes utilities for external black-box model validation.
Frequently asked
- What is SelfExplainML/PiML-Toolbox?
- A Python toolbox for building, validating, and diagnosing interpretable machine learning models through low-code interfaces.
- Is PiML-Toolbox open source?
- Yes — SelfExplainML/PiML-Toolbox is open source, released under the Apache-2.0 license.
- What language is PiML-Toolbox written in?
- SelfExplainML/PiML-Toolbox is primarily written in Jupyter Notebook.
- How popular is PiML-Toolbox?
- SelfExplainML/PiML-Toolbox has 1.3k stars on GitHub.
- Where can I find PiML-Toolbox?
- SelfExplainML/PiML-Toolbox is on GitHub at https://github.com/SelfExplainML/PiML-Toolbox.