teradata-shap-explainability

name	Teradata SHAP Explainability
description	SHAP values for model explainability and feature importance

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

-- 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