lime-explainer

# lime-explainer ## Overview LIME-based local explanation skill for individual predictions across tabular, text, and image data using Local Interpretable Model-agnostic Explanations. ## Capabilities - Tabular data explanations - Text classification explanations - Image classification explanations - Submodular pick for representative samples - Custom distance metrics - Kernel width tuning - Feature discretization - Local surrogate model analysis ## Target Processes - Model Interpretability and Explainability Analysis - Model Evaluation and Validation Framework ## Tools and Libraries - LIME - scikit-learn - numpy - PIL/Pillow (for images) ## Input Schema ```json { "type": "object", "required": ["modelPath", "dataType", "instancePath"], "properties": { "modelPath": { "type": "string", "description": "Path to the trained model or prediction function" }, "dataType": { "type": "string", "enum": ["tabular", "text", "image"], "description": "Type of data to explain" }, "instancePath": { "type": "string", "description": "Path to instance(s) to explain" }, "tabularConfig": { "type": "object", "properties": { "trainingDataPath": { "type": "string" }, "featureNames": { "type": "array", "items": { "type": "string" } }, "categoricalFeatures": { "type": "array", "items": { "type": "integer" } }, "classNames": { "type": "array", "items": { "type": "string" } } ...