# Teradata SHAP Explainability

> SHAP values for model explainability and feature importance

- Skill: `teradata-labs/teradata-shap-explainability` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add teradata-labs/teradata-shap-explainability`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teradata-labs/teradata-shap-explainability/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: teradata-labs (https://skillmd.com/u/teradata-labs)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/teradata-labs/teradata-shap-explainability

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# Teradata SHAP Explainability

| Property | Value |
|----------|-------|
| **Skill Name** | Teradata SHAP Explainability |
| **Description** | SHAP values for model explainability and feature importance |
| **Category** | Model Evaluation |
| **Primary Function** | TD_SHAP |
| **Framework** | SQLE
- **Minimum Version**: Teradata 20.00 |

## Core Capabilities

- Automated table structure analysis via DBC.ColumnsV
- Dynamic SQL generation for TD_SHAP
- Complete workflow from data preparation to results interpretation
- Data quality validation and preprocessing guidance
- Parameter optimization and tuning

## Key Parameters

- **IDColumn**: Unique row identifier
- **InputColumns**: Feature columns
- **ResponseColumn**: Target column
- **ModelType**: 'XGBOOST', 'GLM', 'DECISIONFOREST', etc.
- **NSamples**: Number of background samples (default 100)
- **Accumulate**: Columns to pass through

## Use Cases

1. Model interpretability and transparency
2. Feature importance ranking
3. Individual prediction explanations
4. Regulatory compliance (explainable AI)
5. Model debugging and validation

## Example Usage

```sql
-- TD_SHAP execution
SELECT * FROM TD_SHAP (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    ON {USER_DATABASE}.{MODEL_TABLE} AS ModelTable DIMENSION
    USING
    IDColumn ('{ID_COLUMN}')
    InputColumns ('{FEATURE_COLUMNS}')
    ResponseColumn ('{TARGET_COLUMN}')
    ModelType ('{MODEL_TYPE}')         -- 'XGBOOST','GLM','DECISIONFOREST'
    NSamples (100)
    Accumulate ('{ID_COLUMN}')
) AS dt;
```

## Scripts Included

### Core Analytics Scripts
- **`table_analysis.sql`**: Automatic table structure discovery
- **`preprocessing.sql`**: Data preparation and feature engineering
- **`model_training.sql`**: TD_SHAP execution
- **`evaluation.sql`**: Results analysis and metrics
- **`complete_workflow_template.sql`**: End-to-end workflow

### Utility Scripts
- **`data_quality_checks.sql`**: Comprehensive data validation
- **`parameter_tuning.sql`**: Parameter optimization
- **`diagnostic_queries.sql`**: Results diagnostics and interpretation

## Best Practices

- Always run table_analysis.sql first to understand your data structure
- Validate data quality before executing the analytical function
- Use parameter_tuning.sql to find optimal configuration
- Review diagnostic_queries.sql output for model/results validation

## Limitations

- Requires Teradata Vantage 20.00+ with ClearScape Analytics
- Input data must meet function-specific requirements
- Results depend on data quality and parameter configuration

*Teradata SHAP Explainability - ClearScape Analytics skill for Teradata Vantage*