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)
4.80 (298 reviews)
1,906
students
8 hours
content
Jan 2021
last update
$69.99
regular price
What you will 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
Why take this course?
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
Screenshots
Reviews
Gaetano
May 7, 2022
A beautiful dive into the decision tree model, deepened with examples and statistical tests adjacent to it.
Highly recommended!
Sarang
November 14, 2016
Gopal provides a thorough understanding of key Decision Tree concepts and the examples that he goes through are also interesting.
One feedback - It would be good if you could also walk through things like - why you would choose 60 and 30 as max and min elements in a node, max depth of the tree, etc.
Linda
October 30, 2016
This is quite complete and comprehensive decision tree class I've ever taken. It helps to resolve all the questions I have for decision tree modeling.
Thank you.
Nehal
October 8, 2016
He is simply the best out there. He is amongst the only few instructors whose quality is exemplary every single time.
The lucid explanation followed by the industry application adds to the overall learning experience.
Besides you can be rest assured that he is passionate about teaching - he will answer all your queries. I would recommend all his courses. He is better than professional analytics training institute.
He is the only instructor in Udemy for analytics who will make you feel like that you made a great investment by buying his courses.
I request him to make more courses on varying topics
Praveen
July 13, 2016
No concrete explanation of problem we are trying to solve. Directly got into how to do decision trees. Would have been helpful to understand those variables and what problem we are trying to solve using decision trees. Disappointing.
Kathleen
May 17, 2016
So far very clear and concise. I know a bit about decision trees, but have never fully had the rationale for how one path is chosen over the other explained in this way.
Gautam
September 7, 2015
Excellent course for an introduction to the basics, but more examples might have helped. I would suggest you split your R script into separate files for each section, it makes it easier for me to manage it.
Roshan
August 18, 2015
I will recommend this course to anyone who are new to Decision tree and would like to get very good understanding of it. It would have been great if this course elaborates more on Random Forest.
Vikas
June 7, 2015
Gopal has done very good job in compiling this course. The best part is, if you have questions he responds fairly quickly and answers all your questions. The highlight of this course for me was the coverage of advanced pruning techniques, which is now very clear to me. On top of this course I would like to see one business case study(one on regression tree, one on classification, one on CHAID) where all the concepts explained are practically shown. This would make combination of these two courses as one of the best.
Naval
June 7, 2015
I have been using decision trees in my professional assignments for the last 7 years, while developing trees using the statistical software, many questions regarding the back end methods / algorithms use to keep coming in my mind. This course has answered all those questions. Overall a course covering all the theoretical aspects presented in an easy to understandable way..
Anup
May 23, 2015
An excellent course for learning decision tree model building technique. I fully enjoyed the clarity and simplicity in which the course materials are presented. The focus is more on concepts rather than going into mathematical complexities. I recommend this course for anyone who is learning or new to analytic.
Piyush
May 17, 2015
I searched the internet a lot to find information about how decision trees work and are used in practice. However I found that information was scattered and not available in one place. This course not only provided all the information about decision trees in one place but also explained the working of the underlying model that helped further my understanding of the topic
Alok
February 1, 2015
Good course with good explanation. Quality of teaching and material is good which is required. Instructor has covered all details which are required for decision tree. Recommended for all, even a novice in this area can easily understand the nitty gritties of decision trees.
Manish
January 19, 2015
I found this course very useful and would recommend for those who are looking career in Analytics. Course material covers all relevant topics with practical approach. On top of that, instructor explained each point of the course very well with real world examples.
Bhupinder
December 29, 2014
I loved this course. I have done part of it and I am truly satisfied with the content. It has empowered me to get started with alien software like R studio ( though I have working knowledge of SAS) and help with straight yet deep dive approach into practical approach with number of example. Highly recommended if you'd like to start hands on with decision tree in less than 2 hrs
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udemy ID
12/10/2014
course created date
11/22/2019
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