Decision Tree Modeling Using R Training - United States

Decision Tree Modeling Using R Training​ Course Outline

Introduction to Decision Tree

• Decision Tree modeling objective
• Anatomy of a Decision Tree
• Gains from a Decision Tree (KS calculations)
• Definitions related to Objective Segmentations

Data Design for Modelling

• Historical window
• Performance window
• Decide Performance Window Horizon Using Vintage Analysis
• General Precautions related to Data Design

Data Treatment before Modelling

• Data Sanity Check-Contents
• View
• Frequency Distribution
• Means / Uni-variate
• Categorical Variable Treatment
• Missing Value Treatment Guideline
• Capping Guideline

Classification of Tree development and Algorithm details

• Preamble to data
• Installing R package and R studio
• Developing first Decision Tree in R studio
• Find Strength of the model
• Algorithm Behind Decision Tree
• How is a Decision Tree Developed?
• First on Categorical Dependent Variable
• GINI Method
• Steps taken by software programs to learn the classification (develop the tree)

Industry Practice of Classification Tree - Development, Validation and Usage

• Discussion on Project
• Find Strength of the Model
• Steps taken by the Software Program to Implement the Learning on Unseen Data
• Learning More from a Practical Point of View
• Model Validation and Deployment

Regression Tree and Auto Pruning

• Introduction to Pruning
• Steps of Pruning
• Logic of Pruning
• Understand K Fold Validation for Model
• Implement Auto Pruning using R
• Develop Regression Tree
• Interpret the Output
• How is it Different from Linear Regression?
• Advantages and Disadvantages over Linear Regression
• Another Regression Tree Using R

CHAID Algorithm

• Key Features of CART
• Chi-square Statistics
• Implement Chi-square for Decision Tree Development
• Syntax for CHAID using R, and CHAID vs CART

Other Algorithms

• Entropy in the Context of Decision Tree
• ID3
• Random Forest Method
• Using R for Random Forest Method