Decision Tree - Theory, Application and Modeling using R

Analytics (objective segmentation): Learn Data Science (applied statistics) CHAID / CART / GINI/ ID3/ Random Forest etc.
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  • Lectures 47
  • Contents Video: 3.5 hours
    Other: 2.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 12/2014 English

Course Description

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
  • CHAID
  • 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

What are the requirements?

  • The course is fairly simple but it will help if they understand how to read excel formula

What am I going to get from this course?

  • 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

What is the target audience?

  • Data Mining professionals
  • Analytics professionals
  • People seeking job in analytics industry

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Introduction to decision tree
Welcome Note
Preview
02:54
Section Overview
Preview
01:26
Need of a decision tree
09:13
Anatomy of a Decision Tree
09:29
Gain From a Decision Tree
05:22
KS of a decision tree
03:12
Business Application of a Decison tree
Preview
04:21
Defintions related with Objective segmentation
03:31
Decision Tree vs Logistic Regression
06:24
Section PDF
24 pages
Section 2: Demo of Decision Tree development using R
Section Overview
Preview
01:06
Understand The data for Demo
02:59
View resource to download files
02:39
How to download excel files, R program etc?
2 pages
Install R and R Studio
03:48
First Decision Tree in R
Preview
08:57
Second Decision Tree in R
16:27
Section PDF
14 pages
Section 3: Algorithm behind decision tree
Section Overview
Preview
02:33
Intutive Understanding of Numeric Variable Split
03:29
GINI Index of a node
Preview
06:18
GINI Index of a Split
05:27
CART in action : Decide which variable n its value for the split
06:39
Practical approach of Decision Tree Development
06:02
Implementing decision tree model
03:57
Auto Pruning Technique of decision tree development
09:00
K Fold Cross Validation
02:24
Auto Pruning Using R.
08:30
Developing Regression Tree Using R
06:51
Interpret Regression Tree Output
11:04
Another Regression Tree Using R
02:57
CHAID Algorithm
06:39
CHAID vs CART
02:53
Section PDF
51 pages
Appendix Content - Chi Square Statistic
10:53
Appendix Content - Feel The Chi Square Statistic
Preview
07:15
Appendix content - Degree of freedom of a cross tab
05:44
Appendix content - Chi Square Distribution
05:38
Appendix content - PDF
27 pages
Section 4: Other algorithm of decision tree development
Section Overview
Preview
00:55
Entropy of a Node
05:24
Entropy of a Split
03:24
ID3 Method
01:42
Random Forest Method
Preview
03:48
R syntax for Random Forest
01:49
Section PDF
19 pages
Closure Note
02:16

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Instructor Biography

Gopal Prasad Malakar, 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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