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.
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.
Our Online Self-Learning Courses offer a self-paced approach, enabling participants to start learning at their preferred times. These courses provide structured training and incorporate review exercises to solidify understanding. You'll access instructional videos and PowerPoint presentations (PPTs) and engage in assignments, projects, and various activities aimed at improving learning outcomes, all tailored to your convenience.
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.
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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.
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
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
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
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.
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
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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After completing the project successfully (as reviewed by a CertHippo expert), you will be given CertHippo Decision Tree Modeling in R Expert certificate.
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.
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.
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You can give us a CALL at +1 302 956 2015 (US) OR email at info@certhippo.com
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.
You should be able to do the following after finishing the Decision Tree course at CertHippo:
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 modeling
7. Interpret and Implement Decision Tree model
8. Implement Decision Trees to derive business insights
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
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
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.
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
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