Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 1 Minute(s)
Language
English
Taught by
Gopal Prasad Malakar
Rating
2 views
Course Overview
Analytics/ Supervised Machine Learning/ Data Science: CHAID / CART / Random Forest etc. workout (Python demo at the end)
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
Course Content
- 5 section(s)
- 71 lecture(s)
- Section 1 Introduction to decision tree
- Section 2 1 A : Model Design - Ensure actionable data for modeling
- Section 3 Demo of Decision Tree development using R
- Section 4 Algorithm behind decision tree
- Section 5 Other algorithm of decision tree development
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
Skills covered in this course
Reviews
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CChan Wai Kit
Very Good a step by step guide for decision tree and the multiple methods
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AAjay Ramaseshan
It taught me the basics of decision trees and filled in the missing portions of my understanding. Only negative point was that the lecture delivery the speaking speed of the instructor was a little slow. And yes advanced topics like C4.5 were not covered.
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RRohit Rajan
This course is really good and instructor is also good. However the only low point is that the explanations are usually made using statistical concepts. it will be good to include a detailed and not just basic understanding and definition of these concepts towards the beginning of each course so that the course becomes even more efficient and can cover a much wider audience.
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NNirupama Palyam
Most places explanation was found repeat. I can understand the instructor may be explaining same with different view points. But this sometimes may be confusing to the listener.
