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Decision Tree - Theory, Application and Modeling using R

Analytics (objective segmentation): Learn Data Science (applied statistics) CHAID / CART / GINI/ ID3/ Random Forest etc.
4.7 (54 ratings)
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415 students enrolled
Last updated 2/2017
30-Day Money-Back Guarantee
  • 3.5 hours on-demand video
  • 6 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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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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
Curriculum For This Course
Expand All 47 Lectures Collapse All 47 Lectures 05:52:19
Introduction to decision tree
10 Lectures 45:52

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

Section PDF
24 pages
Demo of Decision Tree development using R
8 Lectures 35:56

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

Check basic understanding of algorithm behind decision tree
6 questions

Section PDF
14 pages
Algorithm behind decision tree
21 Lectures 01:54:13

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

Implementing decision tree model

Auto Pruning Technique of decision tree development

K Fold Cross Validation

Auto Pruning Using R.

Developing Regression Tree Using R

Interpret Regression Tree Output

Another Regression Tree Using R

CHAID Algorithm


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
8 Lectures 19:18

Entropy of a Node

Entropy of a Split

ID3 Method

R syntax for Random Forest

Section PDF
19 pages

Closure Note
About the Instructor
4.3 Average rating
659 Reviews
10,793 Students
10 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 15+ 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.

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