# Teradata XGBoost Analytics

> XGBoost gradient boosting for classification and regression

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

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# Teradata XGBoost Analytics

| Property | Value |
|----------|-------|
| **Skill Name** | Teradata XGBoost Analytics |
| **Description** | XGBoost gradient boosting for classification and regression |
| **Category** | Machine Learning |
| **Primary Function** | TD_XGBoost |
| **Framework** | SQLE |

## Core Capabilities

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

## Key Parameters

- **ResponseColumn**: Target variable column name
- **InputColumns**: Feature columns (comma-separated in quotes)
- **IDColumn**: Unique row identifier
- **ModelType**: 'CLASSIFICATION' or 'REGRESSION'
- **NumBoostedTrees**: Number of boosting rounds (default 100)
- **ShrinkageFactor**: Learning rate 0-1 (default 0.1)
- **MaxDepth**: Maximum tree depth (default 6)
- **MinNodeSize**: Minimum samples in leaf node (default 1)
- **RegLambda**: L2 regularization term (default 1.0)
- **ColumnSubSampling**: Feature subsampling ratio 0-1 (default 1.0)
- **LossFunction**: 'SOFTMAX' (multiclass), 'BINOMIAL' (binary), 'MSE' (regression)
- **Seed**: Random seed for reproducibility

## Use Cases

1. Binary and multiclass classification
2. Regression prediction
3. Feature importance ranking
4. Fraud detection and anomaly scoring
5. Customer churn prediction

## Example Usage

```sql
-- TD_XGBoost execution
SELECT * FROM TD_XGBoost (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    USING
    ResponseColumn ('{TARGET_COLUMN}')
    InputColumns ('{FEATURE_COLUMNS}')
    IDColumn ('{ID_COLUMN}')
    ModelType ('{MODEL_TYPE}')         -- 'CLASSIFICATION' or 'REGRESSION'
    NumBoostedTrees (100)
    ShrinkageFactor (0.1)              -- Learning rate
    MaxDepth (6)
    MinNodeSize (1)
    RegLambda (1.0)                    -- L2 regularization
    ColumnSubSampling (1.0)
    LossFunction ('{LOSS_FUNCTION}')   -- 'SOFTMAX','BINOMIAL','MSE'
    Seed (42)
) 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_XGBoost 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 XGBoost Analytics - ClearScape Analytics skill for Teradata Vantage*