Agent skill
Teradata K-Nearest Neighbors Analytics
K-Nearest Neighbors for classification and regression
Install this agent skill to your Project
npx add-skill https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-knn
SKILL.md
Teradata K-Nearest Neighbors Analytics
| Property | Value |
|---|---|
| Skill Name | Teradata K-Nearest Neighbors Analytics |
| Description | K-Nearest Neighbors for classification and regression |
| Category | Machine Learning |
| Primary Function | TD_KNN |
| Framework | SQLE |
Core Capabilities
- Automated table structure analysis via DBC.ColumnsV
- Dynamic SQL generation for TD_KNN
- Complete workflow from data preparation to results interpretation
- Data quality validation and preprocessing guidance
- Parameter optimization and tuning
Key Parameters
- ResponseColumn: Target variable in training data
- IDColumn: Unique row identifier in test data
- DistanceFeatures: Feature columns for distance computation
- NumberOfNeighbors: K value (default 5)
- VotingMethod: 'UNIFORM' (equal weight) or 'DISTANCE' (inverse-distance)
- DistanceMethod: 'EUCLIDEAN', 'MANHATTAN', 'COSINE'
- Accumulate: Columns to pass through to output
Use Cases
- Classification based on proximity
- Regression with local averaging
- Anomaly detection via distance
- Recommendation systems
- Pattern recognition
Example Usage
-- TD_KNN execution
SELECT * FROM TD_KNN (
ON {USER_DATABASE}.{TRAIN_TABLE} AS TrainTable
ON {USER_DATABASE}.{TEST_TABLE} AS TestTable DIMENSION
USING
ResponseColumn ('{TARGET_COLUMN}')
IDColumn ('{ID_COLUMN}')
DistanceFeatures ('{FEATURE_COLUMNS}')
NumberOfNeighbors (5)
VotingMethod ('UNIFORM') -- 'UNIFORM' or 'DISTANCE'
DistanceMethod ('EUCLIDEAN') -- 'EUCLIDEAN','MANHATTAN','COSINE'
Accumulate ('{ID_COLUMN}')
) AS dt;
Scripts Included
Core Analytics Scripts
table_analysis.sql: Automatic table structure discoverypreprocessing.sql: Data preparation and feature engineeringmodel_training.sql: TD_KNN executionevaluation.sql: Results analysis and metricscomplete_workflow_template.sql: End-to-end workflow
Utility Scripts
data_quality_checks.sql: Comprehensive data validationparameter_tuning.sql: Parameter optimizationdiagnostic_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 K-Nearest Neighbors Analytics - ClearScape Analytics skill for Teradata Vantage
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