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explaining-machine-learning-models

separate

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

When to use

• Understand why a machine learning model made a specific prediction.
• Identify the most important features influencing a model's output.
• Debug model performance issues by identifying unexpected feature interactions.
• Communicate model insights to non-technical stakeholders.
• Ensure fairness and transparency in model predictions.

When not to use

• Model training and hyperparameter search
• Benchmark comparison and threshold selection
• Leakage or prediction-time audits
• Training ownership
• General ML strategy selection

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