Decision Tree Modeling Using R Training in Exeter

Decision Tree Modeling Using R Training​ Course Outline

Module 1: Introduction to Decision Tree

• Decision Tree Modelling Objective
• Anatomy of a Decision Tree
• Important Terminology Related to Decision Trees

Module 2: Overview of R Programming

• R Programming Language
• Data Types
• Control Structures in R

Module 3: Data Treatment Before Modelling

• Data Sanity Check-Contents
• View
• Frequency Distribution
• Uni-Variate
• Categorical Variable Treatment

Module 4: Classification of Tree Development and Algorithm Details

• Installing R Package and R studio
• Developing First Decision Tree in R Studio
• Find Strength of the Model

Module 5: Decision Tree Analysis in Project Management

• Use Decision Tree in Project Management
• Tools for Decision Tree Analysis
• Decision Tree Analysis Strategy

Module 6: Regression Tree and Auto Pruning

• Introduction to Pruning
• Understand K Fold Validation for Model 
• Develop Regression Tree 
• How is it Different from Linear Regression? 
• Advantages and Disadvantages over Linear Regression

Module 7: CHAID Algorithm

• What is CHAID/CART Algorithm?
• Chi-Square Statistics 
• Implement Chi-Square for Decision Tree Development 
• CHAID Vs CART

Module 8: Other Algorithms

• ID3
• Random Forest Method
• Using R for Random Forest Method

Who Should Attend this Decision Tree Modelling Using R Training?

The Decision Tree Modelling Using R Training Course offers a detailed exploration of Decision Tree models, one of the most widely used algorithms in Machine Learning and Data Science. This course can be beneficial for various professionals, including:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• Research Scientists
• Quantitative Researchers
• Risk Assessment Managers
• Predictive Modelers

Prerequisites of the Decision Tree Modelling Using R Training Course

There are no formal prerequisites for attending this Decision Tree Modelling Using R Training Course. However, having some knowledge of the R programming language will be helpful.

Decision Tree Modelling Using R Training Course Overview

Decision Tree Modelling Using R Training introduces delegates to a powerful analytical method for data-driven decision-making. It explains how decision trees support pattern recognition, prediction, and optimisation across industries such as finance, automotive, and telecommunications.

This training supports professionals seeking to strengthen data science capability. It builds the ability to analyse complex datasets, identify meaningful patterns, and derive actionable insights, enabling confident, evidence-based decisions in data-driven environments.

This 1-Day course offered by The Knowledge Academy enables delegates to apply Decision Tree Modelling using R with confidence. Through focused learning, delegates gain the skills to design models, understand algorithms, validate outcomes, and apply best practices in real-world scenarios.

Decision Tree Modelling Using R Training Course Objectives

• To understand the fundamentals of Decision Tree Modelling
• To learn data treatment and frequency distribution techniques
• To explore Decision Tree algorithm development and pruning
• To gain expertise in advanced topics like Random Forest and CHAID Algorithm
• To acquire practical skills in using R for Decision Tree Modelling
• To become proficient in applying Decision Tree Modelling to real-world scenarios

By the end of this course, delegates will emerge with expertise in Decision Tree Modelling using R, empowering them to leverage this powerful tool for data analysis and informed decision-making.

What’s included in this Decision Tree Modelling Using R Training?

• World-Class Training Sessions from Experienced Instructors
• Decision Tree Modelling Using R Certificate
• Digital Delegate Pack