# Teradata ROC Curve Analysis

> ROC curve and AUC analysis for binary classification evaluation

- Skill: `teradata-labs/teradata-roc-curve-analysis` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add teradata-labs/teradata-roc-curve-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teradata-labs/teradata-roc-curve-analysis/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-roc-curve-analysis

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# Teradata ROC Curve Analysis

| Property | Value |
|----------|-------|
| **Skill Name** | Teradata ROC Curve Analysis |
| **Description** | ROC curve and AUC analysis for binary classification evaluation |
| **Category** | Model Evaluation |
| **Primary Function** | TD_ROC |
| **Framework** | SQLE |

## 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

```sql
-- 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*