Decision Tree - Theory, Application and Modeling using R
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Decision Tree - Theory, Application and Modeling using R

Analytics/ Supervised Machine Learning/ Data Science (applied statistics): CHAID / CART / GINI/ ID3/ Random Forest etc.
Best Seller
4.3 (84 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
685 students enrolled
Last updated 8/2017
Current price: $10 Original price: $40 Discount: 75% off
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  • 6 hours on-demand video
  • 2 Articles
  • 14 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Get Crystal clear understanding of decision tree
  • Understand the business scenarios where decision tree is applicable
  • Become comfortable to develop decision tree using R statistical package
  • Understand the algorithm behind decision tree i.e. how does decision tree software work
  • Understand the practical way of validation, auto validation and implementation of decision tree
View Curriculum
  • The course is fairly simple but it will help if they understand how to read excel formula

What is this course?

Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building.

This course ensures that student get understanding of

  • what is the decision tree
  • where do you apply decision tree
  • what benefit it brings
  • what are various algorithm behind decision tree
  • what are the steps to develop decision tree in R
  • how to interpret the decision tree output of R

Course Tags

  • Decision Tree
  • CART
  • Objective segmentation
  • Predictive analytics
  • ID3
  • GINI

Material in this course

  • the videos are in HD format
  • the presentation used to create video are available to download in PDF format
  • the excel files used is available to download
  • the R program used is also available to download

How long the course should take?

It should take approximately 8 hours to internalize the concepts and become comfortable with the decision tree modeling using R

The structure of the course

Section 1 – motivation and basic understanding

  • Understand the business scenario, where decision tree for categorical outcome is required
  • See a sample decision tree – output
  • Understand the gains obtained from the decision tree
  • Understand how it is different from logistic regression based scoring

Section 2 – practical (for categorical output)

  • Install R - process
  • Install R studio - process
  • Little understanding of R studio /Package / library
  • Develop a decision tree in R
  • Delve into the output

Section 3 – Algorithm behind decision tree

  • GINI Index of a node
  • GINI Index of a split
  • Variable and split point selection procedure
  • Implementing CART
  • Decision tree development and validation in data mining scenario
  • Auto pruning technique
  • Understand R procedure for auto pruning
  • Understand difference between CHAID and CART
  • Understand the CART for numeric outcome
  • Interpret the R-square meaning associated with CART

Section 4 – Other algorithm for decision tree

  • ID3
  • Entropy of a node
  • Entropy of a split
  • Random Forest Method

Why take this course?

Take this course to

  • Become crystal clear with decision tree modeling
  • Become comfortable with decision tree development using R
  • Hands on with R package output
  • Understand the practical usage of decision tree
Who is the target audience?
  • Data Mining professionals
  • Analytics professionals
  • People seeking job in analytics industry
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Curriculum For This Course
68 Lectures
Introduction to decision tree
11 Lectures 46:13

Need of a decision tree

Anatomy of a Decision Tree

Gain From a Decision Tree

KS of a decision tree

Defintions related with Objective segmentation

Decision Tree vs Logistic Regression

Check basic understanding of decision tree
12 questions

FAQ: for Introduction section

Section PDF
24 pages
1 A : Model Design - Ensure actionable data for modeling
6 Lectures 38:10

Model Design in Principal

Model Design Precautions

Model Design Outcome

Performance Window Design

Check basic understanding of model design
12 questions

Data Audit n Treatment Guideline and section PDF
Demo of Decision Tree development using R
12 Lectures 01:10:12

Understand The data for Demo

View resource to download files

How to download excel files, R program etc?
2 pages

Install R and R Studio

Second Decision Tree in R

Practical_Usage_of_classification_tree - demo

Practical_Usage_of_classification_tree - assignment

Practical_Usage_of_classification_tree - assignment solution

Section PDF
14 pages
Algorithm behind decision tree
30 Lectures 02:55:21

Intutive Understanding of Numeric Variable Split

GINI Index of a Split

CART in action : Decide which variable n its value for the split

Practical approach of Decision Tree Development

Some practical situation of decision tree model validation

Implementing decision tree model

Auto Pruning Technique of decision tree development part 1

Auto Pruning Technique of decision tree development part 2

K Fold Cross Validation

Auto Pruning Using R.

Developing Regression Tree Using R

Interpret Regression Tree Output

Another Regression Tree Using R

Practical_Usage_of_Regression_tree - demo part 1

Practical_Usage_of_Regression_tree - demo part 2

Practical_Usage_of_Regression_tree - assignment

Practical_Usage_of_Regression_tree - assignment solution

CHAID Algorithm


Check basic understanding of algorithm behind decision tree
14 questions

FAQ - for algorithm behind decision tree section

Section PDF
51 pages

Appendix Content - Chi Square Statistic

Appendix content - Degree of freedom of a cross tab

Appendix content - Chi Square Distribution

Appendix content - PDF
27 pages
Other algorithm of decision tree development
9 Lectures 22:01

Entropy of a Node

Entropy of a Split

ID3 Method

R syntax for Random Forest

Check basic understanding of algorithm behind decision tree -01
15 questions

Section PDF
19 pages

Closure Note
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
1,788 Reviews
22,149 Students
16 Courses
Credit Card Analytics Professional- Trains Machine Learning

I am a seasoned Analytics professional with 16+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.