ai-assurance-toolkit 0.1.0

A Python toolkit for evaluating AI model reliability, performance, and deployment readiness.

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.