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Course information

Decision Tree Modeling Using R Training​ Course Outline

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

Data Design for Modelling

  • Historical window
  • Performance window
  • Decide Performance Window Horizon Using Vintage Analysis
  • General Precautions related to Data Design

Data Treatment before Modelling

  • Data Sanity Check-Contents
  • View
  • Frequency Distribution
  • Means / Uni-variate
  • Categorical Variable Treatment
  • Missing Value Treatment Guideline
  • Capping Guideline

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)

Industry Practice of Classification Tree - Development, Validation and Usage

  • Discussion on Project
  • Find Strength of the Model
  • Steps taken by the Software Program to Implement the Learning on Unseen Data
  • Learning More from a Practical Point of View
  • Model Validation and Deployment

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 is it Different from Linear Regression?
  • Advantages and Disadvantages over Linear Regression
  • Another Regression Tree Using R

CHAID Algorithm

  • Key Features of CART
  • Chi-square Statistics
  • Implement Chi-square for Decision Tree Development
  • Syntax for CHAID using R, and CHAID vs CART

Other Algorithms

  • Entropy in the Context of Decision Tree
  • ID3
  • Random Forest Method
  • Using R for Random Forest Method

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Prerequisites

Basic knowledge of R programming language required before attending this course.

Audience

This training course is ideal for anyone; however, Professionals and Students who want to enter the Analytics Industry. This course is also ideal for Analytics Professionals and Data Mining Professionals.

Decision Tree Modeling Using R Training​ Course Overview

Decision Tree Modeling Using R is a popular Analytic technique which can be implemented in various business fields such as money lending business, automobile, and telecom.

This 1-day Decision Tree Modeling Using R Certification course is designed to provide delegates with a solid understanding of various concepts such as Data treatment before modelling frequency distribution, the algorithm behind decision tree, how is a decision tree developed, GINI method, steps of pruning, ID3, random forest method, and more. Starting from fundamentals of Decision Tree delegates will learn other advance topics such as data design for modelling, data treatment before modelling, classification of tree development and algorithm details, industry practice of classification tree - development, validation and usage, understand K fold validation for the model, CHAID Algorithm, the syntax for CHAID using R, and CHAID vs CART, using R for Random forest method and more.

After attending this course, delegates will gain expertise in Decision Tree Modeling using the R programming language.

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  • Delegate pack consisting of course notes and exercises
  • Manual
  • Experienced Instructor

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Why choose us

Ways to take this course

Our easy to use Virtual platform allows you to sit the course from home with a live instructor. You will follow the same schedule as the classroom course, and will be able to interact with the trainer and other delegates.

Our fully interactive online training platform is compatible across all devices and can be accessed from anywhere, at any time. All our online courses come with a standard 90 days access that can be extended upon request. Our expert trainers are constantly on hand to help you with any questions which may arise.

This is our most popular style of learning. We run courses in 1200 locations, across 200 countries in one of our hand-picked training venues, providing the all important ‘human touch’ which may be missed in other learning styles.

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Highly experienced trainers

All our trainers are highly qualified, have 10+ years of real-world experience and will provide you with an engaging learning experience.

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State of the art training venues

We only use the highest standard of learning facilities to make sure your experience is as comfortable and distraction-free as possible

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Small class sizes

We limit our class sizes to promote better discussion and ensuring everyone has a personalized experience

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Great value for money

Get more bang for your buck! If you find your chosen course cheaper elsewhere, we’ll match it!

This is the same great training as our classroom learning but carried out at your own business premises. This is the perfect option for larger scale training requirements and means less time away from the office.

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Tailored learning experience

Our courses can be adapted to meet your individual project or business requirements regardless of scope.

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Maximise your training budget

Cut unnecessary costs and focus your entire budget on what really matters, the training.

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Team building opportunity

This gives your team a great opportunity to come together, bond, and discuss, which you may not get in a standard classroom setting.

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Monitor employees progress

Keep track of your employees’ progression and performance in your own workspace.

What our customers are saying

Frequently asked questions

FAQ's

Please arrive at the venue at 8:45am.
Basic knowledge of R programming language required before attending this course.
This training course is ideal for anyone; however, Professionals and Students who want to enter the Analytics Industry. This course is also ideal for Analytics Professionals and Data Mining Professionals.
We are able to provide support via phone & email prior to attending, during and after the course.
Delegate pack consisting of course notes and exercises, Manual, Experienced Instructor, and Refreshments
This course is 1 days.
Once your booking has been placed and confirmed, you will receive an email which contains your course location, course overview, pre-course reading material (if required), course agenda and payment receipts
The price for Decision Tree Modeling Using R Training certification in Philippines starts from $1195
The Knowledge Academy is the Leading global training provider in the world for Decision Tree Modeling Using R Training.

Why choose us

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Best price in the industry

You won't find better value in the marketplace. If you do find a lower price, we will beat it.

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Many delivery methods

Flexible delivery methods are available depending on your learning style.

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High quality resources

Resources are included for a comprehensive learning experience.

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"Really good course and well organised. Trainer was great with a sense of humour - his experience allowed a free flowing course, structured to help you gain as much information & relevant experience whilst helping prepare you for the exam"

Joshua Davies, Thames Water

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"...the trainer for this course was excellent. I would definitely recommend (and already have) this course to others."

Diane Gray, Shell

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