Decision Tree - Theory, Application and Modeling using R

What does the course offer?

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 topics

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

Course Material

• 40 HD videos
• 3 Excel files
• 1 R program file
• 5 PDF files

Who should take this course?

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

What makes this course unique?

• Pictorial explanations make it easy to grasp the concepts

• Step by step demonstration in R ensures that students become comfortable with decision tree development
• it covers practical aspects of usage and decision tree development

Why should learners enroll in this course?

To master concepts and become comfortable with decision tree development using R

What will student learn after completion of this course?

They will

• 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