Teradata XGBoost Analytics

Core Capabilities

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

Key Parameters

• ResponseColumn: Target variable column name
• InputColumns: Feature columns (comma-separated in quotes)
• IDColumn: Unique row identifier
• ModelType: 'CLASSIFICATION' or 'REGRESSION'
• NumBoostedTrees: Number of boosting rounds (default 100)
• ShrinkageFactor: Learning rate 0-1 (default 0.1)
• MaxDepth: Maximum tree depth (default 6)
• MinNodeSize: Minimum samples in leaf node (default 1)
• RegLambda: L2 regularization term (default 1.0)
• ColumnSubSampling: Feature subsampling ratio 0-1 (default 1.0)
• LossFunction: 'SOFTMAX' (multiclass), 'BINOMIAL' (binary), 'MSE' (regression)
• Seed: Random seed for reproducibility

Use Cases

1. Binary and multiclass classification
2. Regression prediction
3. Feature importance ranking
4. Fraud detection and anomaly scoring
5. Customer churn prediction

Example Usage

-- TD_XGBoost execution
SELECT * FROM TD_XGBoost (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    USING
    ResponseColumn ('{TARGET_COLUMN}')
    InputColumns ('{FEATURE_COLUMNS}')
    IDColumn ('{ID_COLUMN}')
    ModelType ('{MODEL_TYPE}')         -- 'CLASSIFICATION' or 'REGRESSION'
    NumBoostedTrees (100)
    ShrinkageFactor (0.1)              -- Learning rate
    MaxDepth (6)
    MinNodeSize (1)
    RegLambda (1.0)                    -- L2 regularization
    ColumnSubSampling (1.0)
    LossFunction ('{LOSS_FUNCTION}')   -- 'SOFTMAX','BINOMIAL','MSE'
    Seed (42)
) 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_XGBoost 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 17.20+ with ClearScape Analytics
• Input data must meet function-specific requirements
• Results depend on data quality and parameter configuration

Teradata XGBoost Analytics - ClearScape Analytics skill for Teradata Vantage