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Decision Tree - Theory, Application and Modeling using R

5.5 hours on-demand video

$ 12.99

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Brief Introduction

Analytics/ Supervised Machine Learning/ Data Science (applied statistics): CHAID / CART / GINI/ ID3/ Random Forest etc.

Description

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

Requirements

Requirements
The course is fairly simple but it will help if they understand how to read excel formula

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