td-decision-forest

name	td-decision-forest
description	Decision forest ensemble classifier for robust predictions

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, 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

-- 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: 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.