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Decision Tree - Theory, Application and Modeling using R


Udemy Coupon - Decision Tree - Theory, Application and Modeling using R, Analytics/ Supervised Machine Learning/ Data Science: CHAID / CART / Random Forest etc. workout (Python demo at the end)
  • Created by Gopal Prasad Malakar
  • English

What you'll 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


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
Objective segmentation
Predictive analytics
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
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 this course is for:

Data Mining professionals
Analytics professionals
People seeking job in analytics industry

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