Decision Tree Modeling using R Certification Training
Access Duration - 365 Days
Awarded By Edureka
Guided Learning Hours (20)
Course Material
Number of Modules 08
Self-study Online
4.5( 2 REVIEWS )
103 STUDENTS
Awarded by
What Will I Learn?
Gain a comprehensive understanding of a Decision Tree
Learn how to implement Decision Tree techniques
Learn how to develop a Decision Tree using the R platform
Implement a Decision Tree to gain business insight
Learn how to perform Decision Tree model validation
Overview
Decision Tree Modeling is a popular analytic technique and learning algorithm. This algorithm is implemented in a number of business sectors which include finance, telecom and automobile. If you want to gain an overview of the Decision Tree algorithm, the Decision Tree Modeling using R Certification Training is a great course to get started on. It is designed to provide learners with the skills and knowledge to become an expert in analytics. You will understand the benefits of this technique, how to implement this algorithm and perform validation.
Decision Tree Modeling using R Certification Training will provide a wealth of knowledge on the concepts of Applicability, Description, Shuffling Pattern and Structure. Learners will gain an in-depth understanding of the frequency of Design Patterns used in MapReduce. The course will also teach you about Filtering Patterns, Meta and Graph Patterns, Data Organisation Patterns and Input Output Pattern.
Decision Tree Modeling using R Certification Training will also introduce learners to advanced concepts including CART, CHAID, Pruning, Regression Tree and data designing. Qualifying in this course will set you in the right direction, giving you the opportunity to open the door to new and exciting job roles in data and analytics.
Why You Should Consider Taking this Course at Global Edulink?
Global Edulink is a leading online provider for several accrediting bodies, and provides learners the opportunity to take this exclusive course awarded by Edureka. At Global Edulink, we give our fullest attention to our learners’ needs and ensure they have the necessary information required to proceed with the Course. Learners who register will be given excellent support, discounts for future purchases and be eligible for a TOTUM Discount card and Student ID card with amazing offers and access to retail stores, the library, cinemas, gym memberships and their favourite restaurants.
- Access Duration
- Who is this Course for?
- Entry Requirement
- Method of Assessment
- Certification
- Awarding Body
- Career Path & Progression
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lessons at any time.
This course is recommended for individuals, who want to have a flourishing career in Data Analytics,
- Decision Tree Analyst
- Decision Tree Expert
- Learners should be over the age of 16, and have a basic understanding of English, ICT and numeracy.
- A sound educational background is recommended
- Learners should have a basic understanding of R programming
In order to complete the Course Decision Tree Modeling using R Certification Training, learners will have to submit an assignment. The assignment will demonstrate your familiarity with the particular subject and will test your ability to apply it in a real-world scenario.
Upon the successful completion of the course, you will be awarded the Decision Tree Modeling using R Certification Training by Edureka.
Edureka is a pioneer in online learning and an agency that has frequently and consistently collaborated with educational and academic institutes. It guarantees expert commitment and is dedicated to learners taking their courses. Edureka is internationally recognised and takes learners a step closer to a leading career in the IT industry.
Decision Tree Modeling using R Certification Training In order to complete the Course 'Insert course name,' learners will have to submit an assignment. The assignment will demonstrate your familiarity with the particular subject and will test your ability to apply it in a real-world scenario. will improve your candidature for a number of jobs in data analytics. You can study further courses in the same field and enhance your academic expertise, and add this as a valuable skillset on your resume. Your skills will be recognised by leading organisations that give you the opportunity to land a well-paying job such as Decision Tree Professionals. Given below are job titles you can compete for, along with the average UK salary per annum according to https://www.glassdoor.com.
- Decision Tree Analyst - £95,000 (Approximately)
Course Curriculum
| 1: Introduction to Decision Tree | |||
| Decision Tree modeling Objective | |||
| Anatomy of a Decision Tree | |||
| Gains from a decision tree (KS calculations) | |||
| Definitions related to objective segmentations | |||
| 2: Data design for Modelling | |||
| Historical window | |||
| Performance window | |||
| Decide performance window horizon using Vintage analysis | |||
| General precautions related to data design | |||
| 3: Data treatment before Modelling | |||
| Data sanity check-Contents | |||
| View | |||
| Frequency Distribution | |||
| Means / Uni-variate | |||
| Categorical variable treatment | |||
| Missing value treatment guideline | |||
| capping guideline | |||
| 4: Classification of Tree development and Algorithm details | |||
| 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 | |||
| 5: Industry practice of Classification tree-Development, Validation and Usage | |||
| 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. | |||
| 6: Regression Tree and Auto Pruning | |||
| 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 | |||
| 7: CHAID Algorithm | |||
| Key features of CART | |||
| Chi square statistics | |||
| Implement Chi square for decision tree development | |||
| Syntax for CHAID using R | |||
| CHAID vs CART | |||
| 8: Other Algorithms | |||
| Entropy in the context of decision tree | |||
| ID3 | |||
| Random Forest Method and Using R for Random forest method | |||
| Project work | |||
Sophia Mcdonald
I’m pleased to say that I was able to prepare for the exam and complete it successfully. The course focused on teaching concepts such as Data Design and Regression Tree to name a few.
Ernest Macdonald
It was a fantastic course and I had such a great time studying it. It was made enjoyable from start to finish.