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
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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)
Table of contents
What is this course?
You will learn
✓ 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
This course is for
• Analytics professionals
• People seeking job in analytics industry
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, business intelligence systems like tableau /SAS Visual Analytics, MS access based database application development, Enterprise wide big data framework and streaming analysis.
Please refer to my course for
– SAS / R program details (syntax and options)
– SAS / R output deep dive
– Practical usage in Industrial situation