Decision Tree Modelling Using R

Decision Tree Modelling Using R

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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Decision Tree UpComing Batches

Nov-22 - Jan-03

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Nov-15 - Dec-27

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Dec-04 - Jan-15

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Dec-11 - Jan-22

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Dec-18 - Jan-29

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Dec-25 - Feb-05

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Be future ready. Start learning
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Course Curriculum

Decision Tree Modeling Using R Certification Training

SELF PACED

OL Tech Edu's 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.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Decision Tree Modeling Using R Certification Training

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. 

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Projects

How will I execute the practicals?

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

Course Description


About The Course
Target Audience. The course is designed for professionals who want to learn Decision Tree modelling and apply the modelling techniques using R. They are:
  • Developers who want to step-up as 'Data Scientists.
  • Analytics Consultants.
  • R / SAS / SPSS Professionals.
  • Data Analysts.
  • Information Architects and Data Engineers.
  • Statisticians.

Objectives Of Our Online PySpark Training Course
Prerequisites.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.

Go For Online Spark Training
Access Timeframe.You will get lifetime access to all the videos,discussion forum and other learning contents inside the Learning Management System.

Course Certification

OL Tech Edu’s Certificate Holders work at top 500s of companies like

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

Course FAQ's

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