Decision Tree Modeling Using R Certification Training

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Decision Tree Modeling Using R Certification Training

Master topics such as data design, regression tree, pruning, and numerous algorithms such as CHAID, CART, ID3, GINI, and Random forest to become a Decision Tree Modeling expert using R.

Why Enroll In Course?

Decision Tree Modeling Using R Certification Training can enhance your knowledge and skills in decision tree modeling, machine learning, and programming in R. This can improve your career prospects by differentiating you from other candidates in the competitive job market and increasing your chances of securing high-paying jobs. Ultimately, the training can provide you with the necessary skills, knowledge, and recognition to advance your career in data science and predictive analytics.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Introduction to Decision Tree

Learning Objectives - In this module, you will learn what a Decision Tree is and why it is useful. What are the primary goals of Decision Tree modeling? How does one grasp the benefits of the Decision Tree and how does one implement it in business scenarios? Topics: include Decision Tree modeling Objective, Architecture of a Decision Tree, Decision Tree Gains (KS Calculations), and Definitions of Objective Segments.

Data design for Modeling

Learning Objective- This session will teach you how to create data for modeling.
Topics- Performance window, History window Using Vintage analysis, determine the performance window horizon. Precautions for general data design

Data treatment before Modelling

Learning Objective- In this topic, you will learn how to ensure Data Sanity and how to execute the essential tests prior to modeling.
Topics- Contents, view, frequency distribution, means / univariate, categorical variable treatment, missing value treatment guideline, capping guideline

Classification of Tree development and Algorithm details

Learning Objectives - In this module, you will learn to use R and the Algorithm to develop the Decision Tree.
Topics - As a preamble to the data, After installing the R package and R studio, In R studio, I'm creating my first Decision Tree. Determine the model's strength. The Decision Tree Algorithm How is a Decision Tree constructed? , First, consider the categorical dependent variable. The GINI Method, Software programmers' actions to learn categorization (develop the tree), Decision tree assignment

Industry practice of Classification tree-Development, Validation and Usage

Learning Objectives - This subject will teach you how classification trees are created, validated, and used in industry.

Topics - Assignment discussion, Determine the model's strength. Actions done by a software programmer to execute learning on previously unknown data, learning more from a practical standpoint, Validation and deployment of the model.

Regression Tree and Auto Pruning

Learning Objectives - This session will teach you about a decision tree's advanced stopping criteria. You will also learn how to create Decision Trees for a variety of outcomes.

Topics - Pruning Overview, Pruning Procedures, Pruning logic, Learn about K fold validation for models. Use R to implement Auto Pruning. Create a Regression Tree, Evaluate the results, How it differs from Linear Regression, the benefits and drawbacks of Linear Regression, Another R-based Regression Tree

CHAID Algorithm

Learning Objectives - In this module, you will discover what Chi square and CHAID are, how they function, and the difference between CHAID and CART, among other things.

Topics - CART's key characteristics, Chi square statistics, Use Chi square to create decision trees, R syntax for CHAID, and CHAID versus CART.

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Certification

EduCerts Certification Process:

After completing the project successfully (as reviewed by a EduCerts expert), you will be given EduCerts Decision Tree Modeling in R Expert certificate.

Online Training FAQs

How soon after Signing up would I get access to the Learning Content?

Your LMS (Learning Management System) access will be active as soon as you enroll in the course. You will instantly have access to our course content in the form of Videos, PPTs, PDFs, and Assignments. You may begin learning right away.

Will I Get Placement Assistance?

We have included a resume creation feature in your LMS to assist you in this attempt. You may now design a winning CV in just three simple steps. You will have unrestricted access to these templates across all roles and designations. All you have to do is sign in to your LMS and select the "make your resume" option.

What are the payment options?

All major banks accept credit cards, debit cards, and net banking. We make use of the CCAvenue Payment Gateway. You may pay in USD using PayPal. There are also EMI choices available

Course Description

About The Course

The Decision Tree Modelling course is intended to give students the information and abilities needed to become Predictive Analytics experts. The course curriculum covers fundamental ideas such as the need for a model and data design, as well as advanced topics like the Regression Tree, Pruning, CHAID, and CART algorithms.

Course Objectives

You should be able to do the following after finishing the Decision Tree course at EduCerts:

1. Understand the Anatomy of a Decision Tree

Learn to use the R platform to develop Decision Trees
Apply various Decision Tree techniques (CHAID / CART etc.)
Perform Decision Tree Model Validation
Learn where to use CHAID / CART / ID3,etc.
Learn to design data for Decision Tree modeling
Interpret and Implement Decision Tree model
Implement Decision Trees to derive business insights

Why learn Decision Tree?

1. What is core Analytics work

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

3. Why modeling 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

Who should go for this course?

The course is intended for professionals who wish to understand Decision Tree modeling and apply it using R. They are as follows:

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

What are the prerequisites for this Course?

This course requires a basic understanding of the R programming language. This course will solely cover the R programming syntax necessary for the building of a Decision Tree model.

Projects

How will I execute the Practical's?

We will assist you in installing the CertHippo Virtual Machine on your system for your practical work. This will be a local connection for you. The installation instructions are available in LMS