# Teradata K-Nearest Neighbors Analytics

> K-Nearest Neighbors for classification and regression

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

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# 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

1. Classification based on proximity
2. Regression with local averaging
3. Anomaly detection via distance
4. Recommendation systems
5. Pattern recognition

## Example Usage

```sql
-- 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 discovery
- **`preprocessing.sql`**: Data preparation and feature engineering
- **`model_training.sql`**: TD_KNN 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 K-Nearest Neighbors Analytics - ClearScape Analytics skill for Teradata Vantage*