# Td Decision Forest

> Decision forest ensemble classifier for robust predictions

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

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# Teradata Decision Forest Analytics

| **Skill Name** | Teradata Decision Forest Analytics |
|----------------|--------------|
| **Description** | Decision forest ensemble classifier for robust predictions |
| **Category** | Classification Analytics |
| **Function** | TD_DecisionForest |

## Core Capabilities

- **Complete analytical workflow** from data exploration to model deployment
- **Automated preprocessing** including scaling, encoding, and train-test splitting
- **Advanced TD_DecisionForest implementation** with parameter optimization
- **Comprehensive evaluation metrics** and model validation
- **Production-ready SQL generation** with proper table management
- **Error handling and data quality checks** throughout the pipeline
- **Business-focused interpretation** of analytical results

## Table Analysis Workflow

This skill automatically analyzes your provided table to generate optimized SQL workflows. Here's how it works:

### 1. Table Structure Analysis
- **Column Detection**: Automatically identifies all columns and their data types
- **Data Type Classification**: Distinguishes between numeric, categorical, and text columns
- **Primary Key Identification**: Detects unique identifier columns
- **Missing Value Assessment**: Analyzes data completeness

### 2. Feature Engineering Recommendations
- **Numeric Features**: Identifies columns suitable for scaling and normalization
- **Categorical Features**: Detects columns requiring encoding (one-hot, label encoding)
- **Target Variable**: Helps identify the dependent variable for modeling
- **Feature Selection**: Recommends relevant features based on data types

### 3. SQL Generation Process
- **Dynamic Column Lists**: Generates column lists based on your table structure
- **Parameterized Queries**: Creates flexible SQL templates using your table schema
- **Table Name Integration**: Replaces placeholders with your actual table names
- **Database Context**: Adapts to your database and schema naming conventions

## How to Use This Skill

1. **Provide Your Table Information**:
   ```
   "Analyze table: database_name.table_name"
   or
   "Use table: my_data with target column: target_var"
   ```

2. **The Skill Will**:
   - Query your table structure using `SHOW COLUMNS FROM table_name`
   - Analyze data types and suggest appropriate preprocessing
   - Generate complete SQL workflow with your specific column names
   - Provide optimized parameters based on your data characteristics

## Input Requirements

### Data Requirements
- **Source table**: Teradata table with analytical data
- **Target column**: Dependent variable for classification analysis
- **Feature columns**: Independent variables (numeric and categorical)
- **ID column**: Unique identifier for record tracking
- **Minimum sample size**: 100+ records for reliable classification modeling

### Technical Requirements
- **Teradata Vantage** with ClearScape Analytics enabled
- **Database permissions**: CREATE, DROP, SELECT on working database
- **Function access**: TD_DecisionForest, TD_DecisionForestPredict

## Output Formats

### Generated Tables
- **Preprocessed data tables** with proper scaling and encoding
- **Train/test split tables** for model validation
- **Model table** containing trained TD_DecisionForest parameters
- **Prediction results** with confidence metrics
- **Evaluation metrics** table with performance statistics

### SQL Scripts
- **Complete workflow scripts** ready for execution
- **Parameterized queries** for different datasets
- **Table management** with proper cleanup procedures

## Classification Use Cases Supported

1. **Ensemble classification**: Comprehensive analysis workflow
2. **Feature importance**: Comprehensive analysis workflow
3. **Robust predictions**: Comprehensive analysis workflow

## Best Practices Applied

- **Data validation** before analysis execution
- **Proper feature scaling** and categorical encoding
- **Train-test splitting** with stratification when appropriate
- **Cross-validation** for robust model evaluation
- **Parameter optimization** using systematic approaches
- **Residual analysis** and diagnostic checks
- **Business interpretation** of statistical results
- **Documentation** of methodology and assumptions

## Example Usage

```sql
-- Example workflow for Teradata Decision Forest Analytics
-- Replace 'your_table' with actual table name

-- 1. Data exploration and validation
SELECT COUNT(*),
       COUNT(DISTINCT your_id_column),
       AVG(your_target_column),
       STDDEV(your_target_column)
FROM your_database.your_table;

-- 2. Execute complete classification workflow
-- (Detailed SQL provided by the skill)
```

## Scripts Included

### Core Analytics Scripts
- **`preprocessing.sql`**: Data preparation and feature engineering
- **`table_analysis.sql`**: Automatic table structure analysis
- **`complete_workflow_template.sql`**: End-to-end workflow template
- **`model_training.sql`**: TD_DecisionForest training procedures
- **`prediction.sql`**: TD_DecisionForestPredict execution
- **`evaluation.sql`**: Model validation and metrics calculation

### Utility Scripts
- **`data_quality_checks.sql`**: Comprehensive data validation
- **`parameter_tuning.sql`**: Systematic parameter optimization
- **`diagnostic_queries.sql`**: Model diagnostics and interpretation

## Limitations and Disclaimers

- **Data quality**: Results depend on input data quality and completeness
- **Sample size**: Minimum sample size requirements for reliable results
- **Feature selection**: Manual feature engineering may be required
- **Computational resources**: Large datasets may require optimization
- **Business context**: Statistical results require domain expertise for interpretation
- **Model assumptions**: Understand underlying mathematical assumptions

## Quality Checks

### Automated Validations
- **Data completeness** verification before analysis
- **Statistical assumptions** testing where applicable
- **Model convergence** monitoring during training
- **Prediction quality** assessment using validation data
- **Performance metrics** calculation and interpretation

### Manual Review Points
- **Feature selection** appropriateness for business problem
- **Model interpretation** alignment with domain knowledge
- **Results validation** against business expectations
- **Documentation** completeness for reproducibility

## Updates and Maintenance

- **Version compatibility**: Tested with latest Teradata Vantage releases
- **Performance optimization**: Regular query performance reviews
- **Best practices**: Updated based on analytics community feedback
- **Documentation**: Maintained with latest ClearScape Analytics features
- **Examples**: Updated with real-world use cases and scenarios

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*This skill provides production-ready classification analytics using Teradata ClearScape Analytics TD_DecisionForest with comprehensive data science best practices.*