AI Assurance Toolkit
AI Assurance Toolkit is a lightweight Python package for evaluating machine learning model reliability, performance, and deployment readiness.
What it does
The toolkit evaluates a trained machine learning model against a labeled test dataset and generates a structured performance report.
Metrics included
- Accuracy
- Precision
- Recall
- F1 score
- False positive rate
- False negative rate
- AUC-ROC
- Calibration / Brier score
- Per-class metrics
- Plain-English deployment signal
Installation
pip install ai-assurance-toolkit
Quick start
ai-assurance evaluate \
--model model.pkl \
--dataset test_data.csv \
--target credit_risk \
--model-name "German Credit Risk Classifier"
Example
Generate a test model and sample dataset:
python examples/setup_test_model.py
Then run the evaluator:
ai-assurance evaluate \
--model model.pkl \
--dataset test_data.csv \
--target credit_risk \
--model-name "German Credit Risk Classifier"
Output
The package creates:
module_a_outputs/performance_report.json
Python usage
from ai_assurance_toolkit import run_performance_evaluation
License
Apache License 2.0.
1 maintainer
Metadata
1 maintainer
Release files for ai-assurance-toolkit 0.1.0
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Release files / ai_assurance_toolkit-0.1.0.tar.gz
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| Size | 12.4 kB |
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