ai-critic 0.2.3

Fast AI evaluator for scikit-learn models

Performance under noise

Visualizations are optional and do not affect the decision logic.

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⚙️ Main API

AICritic(model, X, y)

• model: scikit-learn compatible estimator
• X: feature matrix
• y: target vector

evaluate(view="all", plot=False)

• view: "executive", "technical", "details", "all" or custom list
• plot: generates graphs when True

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🧠 What ai-critic Detects

| Category | Risks |

| ------------ | ---------------------------------------- |

| 🔍 Data | Target Leakage, NaNs, Imbalance |

| 🧱 Structure | Excessive Complexity, Overfitting |

| 📈 Validation | Perfect or Statistically Suspicious CV |

| 🧪 Robustness | Stable, Fragile, or Misleading |

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🛡️ Best Practices

• CI/CD: Use executive output as a quality gate
• Iteration: Use technical output during tuning
• Governance: Log detailed output
• Skepticism: Never blindly trust a perfect CV

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🧭 Use Cases

• Pre-deployment Audit
• ML Governance
• CI/CD Pipelines
• Risk Communication for Non-Technical Users

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📄 License

Distributed under the MIT License.

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🧠 Final Note

ai-critic is not a benchmarking tool. It's a decision-making tool.

If a model fails here, it doesn't mean it's bad—it means it shouldn't be trusted yet.