Agent skill

Teradata SMOTE Oversampling

Synthetic Minority Oversampling for imbalanced datasets

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Install this agent skill to your Project

npx add-skill https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-smote

SKILL.md

Teradata SMOTE Oversampling

Property Value
Skill Name Teradata SMOTE Oversampling
Description Synthetic Minority Oversampling for imbalanced datasets
Category Feature Engineering
Primary Function TD_SMOTE
Framework SQLE
- Minimum Version: Teradata 20.00

Core Capabilities

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

Key Parameters

  • InputColumns: Feature columns
  • TargetColumn: Target/class column (identifies minority class)
  • SamplingStrategy: 'smote' (standard), 'adasyn' (adaptive), 'borderline'
  • NearestNeighbors: K for neighborhood (default 5)
  • SamplingPercentage: Oversampling ratio percentage
  • Seed: Random seed
  • Accumulate: Columns to pass through

Use Cases

  1. Handling class imbalance in classification
  2. Fraud detection data augmentation
  3. Rare event prediction improvement
  4. Medical diagnosis with minority classes

Example Usage

sql
-- TD_SMOTE execution
SELECT * FROM TD_SMOTE (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    USING
    InputColumns ('{FEATURE_COLUMNS}')
    TargetColumn ('{TARGET_COLUMN}')
    SamplingStrategy ('smote')         -- 'smote','adasyn','borderline'
    NearestNeighbors (5)
    SamplingPercentage (100)
    Seed (42)
    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_SMOTE 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 SMOTE Oversampling - ClearScape Analytics skill for Teradata Vantage

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