Behavioral Analysis of ML models is the study of model behavior across different input regions, subgroups, and conditions — going beyond aggregate metrics to understand how the model behaves for different types of inputs, revealing biases, inconsistencies, and failure patterns.

Behavioral Analysis Methods

• Subgroup Analysis: Evaluate performance on meaningful subgroups (by tool, product, process window region).
• Error Analysis: Categorize model errors by type and frequency — identify systematic failure patterns.
• Decision Boundary Exploration: Probe the model near decision boundaries to understand classification transitions.
• Counterfactual Analysis: Study how predictions change as individual features are varied.

Why It Matters

• Failure Patterns: Aggregate accuracy hides systematic failures on specific subgroups or input types.
• Bias Detection: Reveals if the model performs differently on different tools, products, or process conditions.
• Process Insight: Error patterns often reveal insights about the underlying process physics.

Behavioral Analysis is understanding the model's personality — comprehensively studying how it behaves across different situations, inputs, and conditions.