A package to perform quality analyses for Machine Learning models
Project description
Quality Analysis for Machine Learning models
The three quality pillars are:
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Functionality: groups analyses that evaluate how ”well“ an AI module performs a given task (i.e. assessing the suitability of an AI module for an application domain).
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Comprehensibility: groups analyses that try to open the blackbox and enable stakeholders (model producers, users, or regulators) to interpret decisions and the decision-making process.
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Robustness: groups analyses that assess how the ML component responds to small changes in the input.
The latest release performs functionality and robustness analyses for image classification and regression models. The next versions will include comprehensibility analysis and accept text data.
Installation
Using the PyPi package:
pip install ml-model-quality-analysis
Notable Dependencies
TensorFlow 2.3 is required:
pip install tensorflow==2.3
Getting Started
For examples on performing quality analysis for ML models, see the Quality Report Notebook.
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