anchors

**Anchors** are an **interpretability method that explains a model's prediction by finding a decision rule (an "anchor") that is sufficient to guarantee the prediction** — if the anchor conditions are met, the prediction is (almost) always the same, regardless of other feature values. **How Anchors Work** - **Rule Format**: IF (feature_1 = value_1) AND (feature_2 = value_2) THEN prediction = class_A (with precision ≥ τ). - **Precision**: The fraction of instances matching the anchor that have the same prediction (e.g., τ = 95%). - **Search**: Use beam search with perturbation-based coverage estimation to find the shortest sufficient anchor. - **Coverage**: The fraction of all instances where the anchor applies — wider coverage = more general rule. **Why It Matters** - **Sufficient Explanations**: Unlike LIME/SHAP (which show feature importance), anchors give sufficient conditions for the prediction. - **Actionable**: An anchor rule is directly actionable — "as long as these conditions hold, the prediction won't change." - **Model-Agnostic**: Works with any classifier — just needs black-box access. **Anchors** are **sufficient explanation rules** — finding the simplest set of conditions that lock in a prediction regardless of other features.