Teradata ROC Curve Analysis

Core Capabilities

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

Key Parameters

• ProbabilityColumn: Column with predicted probabilities
• ObservationColumn: Column with actual class labels
• PositiveClass: Value representing the positive class
• NumThresholds: Number of threshold points (default 50)

Use Cases

1. Binary classifier performance evaluation
2. Threshold selection optimization
3. Model comparison via AUC
4. Trade-off analysis between TPR and FPR

Example Usage

-- TD_ROC execution
SELECT * FROM TD_ROC (
    ON {USER_DATABASE}.{PREDICTION_TABLE} AS InputTable
    USING
    ProbabilityColumn ('{PROBABILITY_COLUMN}')
    ObservationColumn ('{ACTUAL_LABEL_COLUMN}')
    PositiveClass ('{POSITIVE_CLASS_VALUE}')
    NumThresholds (50)
) 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_ROC 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 ROC Curve Analysis - ClearScape Analytics skill for Teradata Vantage