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name: anchors-local-explanations description: "Routes Alibi requests for AnchorTabular, AnchorText, and AnchorImage workflows, including sampling, segmentation, and precision/coverage troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Anchors for Local Explanations

Use this sub-skill when a user wants an anchor rule that explains a specific prediction on tabular data, text, or images.

Trigger phrases

Open this sub-skill when the user mentions:

• anchors or anchor rules
• AnchorTabular, AnchorText, or AnchorImage
• precision, coverage, beam search, or threshold
• text sampling strategies such as unknown, similarity, or language_model
• image segmentation, superpixels, or custom segmentation_fn

What this sub-skill owns

• AnchorTabular
• AnchorText
• AnchorImage
• the text sampling helpers and image-segmentation guidance needed to use them

What it does not own

• SHAP or integrated gradients
• counterfactual methods
• confidence scores or prototype selection
• save/load guidance

Read next

• references/workflows.md for the tabular / text / image route map
• references/api-reference.md for the verified AnchorTabular, AnchorText, and AnchorImage signatures
• references/anchor-text.md for text-specific sampling details
• references/troubleshooting.md for input-shape and dependency failures
• scripts/check_spacy_model.py before using a text anchor workflow
• scripts/smoke_anchor_tabular.py and scripts/smoke_anchor_image.py for safe CPU smokes

Typical flow

1. Decide whether the anchor is tabular, text, or image.
2. Make sure the predictor accepts the input type the method expects.
3. For tabular data, fit the explainer on a representative training set.
4. For text, decide whether nlp or language_model sampling is the right perturbation strategy.
5. For images, choose a segmentation strategy that returns sensible superpixels.
6. Use the smoke scripts on tiny synthetic inputs before trying a larger example.

Good fits

• local explanations for a single prediction
• a rule-based explanation with high precision and explicit coverage
• tabular, text, or image workflows where the user wants human-readable conditions

Common failure signals

• the predictor is not batched or does not accept the right input type
• AnchorTabular was not fitted before explain
• AnchorText is missing a spaCy model or the language_model path is not installed
• image segmentation returns the wrong shape or type
• the result is empty or far too long because the instance sits near a decision boundary