Decision Tree Modelling using R Online Training | Edureka

Decision Tree Modeling Using R Certification Training


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Why should you take Decision Tree Modeling Using R course ?

About the Course

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.

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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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 

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Edureka’s R Programmer with proficiency is Decision Tree Modeling Certificate Holders work at 1000s of companies like

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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.

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.

Assignments

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.

Certification

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

Forum

We have a community forum for all our customers that further facilitates learning through peer interaction and knowledge sharing.
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.
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
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