Decision Tree Modelling using R Online Training | Edureka

Decision Tree Modeling Using R Certification Training

Become a Decision Tree Modeling expert using R platform by mastering concepts like Data design, Regression Tree, Pruning and various algorithms like CHAID, CART, ID3, GINI and Random forest.

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Online self - paced learning

Online Self Learning Courses are designed for self-directed training, allowing participants to begin at their convenience with structured training and review exercises to reinforce learning. You'll learn through videos, PPTs and complete assignments, projects and other activities designed to enhance learning outcomes, all at times that are most convenient to you.

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Course Duration

You will undergo self-paced learning where you will get an in-depth knowledge of various concepts that will be covered in the course.

Real-life Case Studies

Towards the end of the course, you will be working on a project where you are expected to implement the techniques learnt during the course.


Each module will contain practical assignments, which can be completed before going to next module.

Lifetime Access

You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.


edureka certifies you as an expert in Decision Tree Modeling in R based on the project reviewed by our expert panel.


We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
The Decision Tree Modelling course is designed to provide knowledge and skills to become a Predictive analytics expert. Basic concepts like the Need for a model and Data design are covered along with advance concepts like Regression Tree, Pruning, CHAID and CART algorithms in the course curriculum.

After the completion of Decision Tree course at Edureka, you should be able to:

1. Understand the Anatomy of a Decision Tree 

2. Learn to use the R platform to develop Decision Trees

3. Apply various Decision Tree techniques (CHAID / CART etc.) 

4. Perform Decision Tree Model Validation 

5. Learn where to use CHAID / CART / ID3,etc. 

6. Learn to design data for Decision Tree modelling 

7. Interpret and Implement Decision Tree model 

8. Implement Decision Trees to derive business insights

Decision Tree Modelling is a popular Analytic technique. This course can give you a head start on:

1. What is core Analytics work

2. What do they mean, when they talk of model

3. Why modelling is such a beneficial proposition

4. How do you develop decision tree using popular platform of R

5. How do you validate to know, it will work over time

The course is designed for professionals who want to learn Decision Tree modelling and apply the modelling techniques using R. They are:

1. Developers who want to step-up as 'Data Scientists

2. Analytics Consultants 

3. R / SAS / SPSS Professionals 

4. Data Analysts 

5. Information Architects and Data Engineers 

6. Statisticians

The pre-requisite for this course is basic knowledge of R programming language. This course will explain only those R programming syntax which is required for the Decision Tree model development.

For your practical work, we will help you setup Edureka's Virtual Machine in your System. This will be a local access for you. The required installation guide is present in LMS.

Learning Objectives - In this module, you will understand What is a Decision Tree and what are the benefits. What are the core objectives of Decision Tree modelling, How to understand the gains from the Decision Tree and How does one apply the same in business scenarios

Topics - Decision Tree modeling Objective, Anatomy of a Decision Tree, Gains from a decision tree (KS calculations), and Definitions related to objective segmentations

Learning Objectives - In this module, you will learn how to design the data for modelling

Topics - Historical window, Performance window, Decide performance window horizon using Vintage analysis, General precautions related to data design

Learning Objectives - In this module, you will learn how to ensure Data Sanity check and you will also learn to perform the necessary checks before modelling 

Topics - Data sanity check-Contents, View, Frequency Distribution, Means / Uni-variate, Categorical variable treatment, Missing value treatment guideline, capping guideline

Learning Objectives - In this module, you will learn to use R and the Algorithm to develop the Decision Tree. 

Topics - 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), Assignment on decision tree

Learning Objectives - In this module you will understand how Classification trees are Developed, Validated and Used in the industry 

Topics - Discussion on assignment, Find Strength of the model, Steps taken by software program to implement the learning on unseen data, learning more from practical point of view, Model Validation and Deployment.

Learning Objectives - In this module you will understand the Advance stopping criteria of a decision tree. You will also learn to develop Decision Trees for numerous outcomes.

Topics - 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 it is different from Linear Regression, Advantages and Disadvantages over Linear Regression, Another Regression Tree using R

Learning Objectives - In this module you will learn what is Chi square and CHAID and their working and also the difference between CHAID and CART etc.. 

Topics - Key features of CART, Chi square statistics, Implement Chi square for decision tree development, Syntax for CHAID using R, and CHAID vs CART.

Learning Objectives - In this module you will learn about ID3, Entropy, Random Forest and Random Forest using R 

Topics - Entropy in the context of decision tree, ID3, Random Forest Method and Using R for Random forest method, Project work 

. Call a Course Adviser for discussing Curriculum Details . 1844 230 6361
As soon as you enrol into the course, your LMS (The Learning Management System) access will be functional. You will immediately get access to our course content in the form of a complete set of Videos, PPTs, PDFs and Assignments. You can start learning right away.
We do provide placement assistance by routing relevant job opportunities to you as and when they come up. To get notified on relevant opportunities, it is important that you fill out your profile details.

It is important to attend classes and complete assignments. Course completion is an important criterion based on which we screen profiles of learners interested in a particular job. Also, before your profile is shared with prospective employers, you will have to go through an internal assessment by edureka. So it is important to be well versed with the course concepts to become eligible for placement opportunities.
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  • Once you are successfully through the project (Reviewed by a edureka expert), you will be awarded with edureka’s Decision Tree Modeling in R Expert certificate.
  • edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon etc. Please be assured.

Decision Tree Modeling Using R Certification Training