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0.1.0

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ai-assurance-toolkit

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

aiassuranceauditevaluationlearningmachinemodelnistpublicresponsiblermfsector
Published14 Jun 2026
Direct dependencies8
Transitive dependenciesNot calculated

README

Version 0.1.0 documentation

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.

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Declared dependencies 8

Native requirements
8 direct targetsNot calculated transitive count
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ai_assurance_toolkit-0.1.0-py3-none-any.whl

bdist_wheel · 12.07 KB · Python py3

Source file
ai_assurance_toolkit-0.1.0.tar.gz

sdist · 12.12 KB · Python source

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harpiking/ai-assurance-toolkit

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