Decision Tree Theory, Application and Modeling using R

Decision Tree – Theory, Application and Modeling using R, available at $69.99, has an average rating of 4.85, with 71 lectures, 4 quizzes, based on 299 reviews, and has 1937 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Mining professionals or Analytics professionals or People seeking job in analytics industry It is particularly useful for Data Mining professionals or Analytics professionals or People seeking job in analytics industry.

Enroll now: Decision Tree – Theory, Application and Modeling using R

Summary

Title: Decision Tree – Theory, Application and Modeling using R

Price: $69.99

Average Rating: 4.85

Number of Lectures: 71

Number of Quizzes: 4

Number of Published Lectures: 71

Number of Published Quizzes: 4

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 75

Original Price: $29.99

Quality Status: approved

Status: Live

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

Who Should Attend

Data Mining professionals

Analytics professionals

People seeking job in analytics industry

Target Audiences

Data Mining professionals

Analytics professionals

People seeking job in analytics industry

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 Curriculum

Chapter 1: Introduction to decision tree

Lecture 1: Welcome Note

Lecture 2: Section Overview

Lecture 3: Need of a decision tree

Lecture 4: Anatomy of a Decision Tree

Lecture 5: Gain From a Decision Tree

Lecture 6: KS of a decision tree

Lecture 7: Business Application of a Decison tree

Lecture 8: Defintions related with Objective segmentation

Lecture 9: Decision Tree vs Logistic Regression

Lecture 10: FAQ: for Introduction section

Lecture 11: Section PDF

Chapter 2: 1 A : Model Design – Ensure actionable data for modeling

Lecture 1: Section Overview

Lecture 2: Model Design in Principal

Lecture 3: Model Design Precautions

Lecture 4: Model Design Outcome

Lecture 5: Performance Window Design

Lecture 6: Data Audit n Treatment Guideline and section PDF

Chapter 3: Demo of Decision Tree development using R

Lecture 1: Section Overview

Lecture 2: Understand The data for Demo

Lecture 3: View resource to download files

Lecture 4: How to download excel files, R program etc?

Lecture 5: Install R and R Studio

Lecture 6: First Decision Tree in R

Lecture 7: Second Decision Tree in R

Lecture 8: About New additions in the course work

Lecture 9: Practical_Usage_of_classification_tree – demo

Lecture 10: Practical_Usage_of_classification_tree – assignment

Lecture 11: Practical_Usage_of_classification_tree – assignment solution

Lecture 12: Section PDF

Chapter 4: Algorithm behind decision tree

Lecture 1: Section Overview

Lecture 2: Intutive Understanding of Numeric Variable Split

Lecture 3: GINI Index of a node

Lecture 4: GINI Index of a Split

Lecture 5: CART in action : Decide which variable n its value for the split

Lecture 6: Practical approach of Decision Tree Development

Lecture 7: Some practical situation of decision tree model validation

Lecture 8: Implementing decision tree model

Lecture 9: Auto Pruning Technique of decision tree development part 1

Lecture 10: K Fold Cross Validation

Lecture 11: Auto Pruning Using R.

Lecture 12: Developing Regression Tree Using R

Lecture 13: Interpret Regression Tree Output

Lecture 14: Another Regression Tree Using R

Lecture 15: Practical_Usage_of_Regression_tree – demo part 1

Lecture 16: Practical_Usage_of_Regression_tree – demo part 2

Lecture 17: Practical_Usage_of_Regression_tree – assignment

Lecture 18: Practical_Usage_of_Regression_tree – assignment solution

Lecture 19: Linear Regression vs Regression tree

Lecture 20: CHAID Algorithm

Lecture 21: CHAID vs CART

Lecture 22: FAQ – for algorithm behind decision tree section

Lecture 23: Section PDF

Lecture 24: Appendix Content – Chi Square Statistic

Lecture 25: Appendix Content – Feel The Chi Square Statistic

Lecture 26: Appendix content – Degree of freedom of a cross tab

Lecture 27: Appendix content – Chi Square Distribution

Lecture 28: Appendix content – PDF

Chapter 5: Other algorithm of decision tree development

Lecture 1: Section Overview

Lecture 2: Entropy of a Node

Lecture 3: Entropy of a Split

Lecture 4: ID3 Method

Lecture 5: Random Forest Method

Lecture 6: R syntax for Random Forest

Lecture 7: Ensemble Learning – Bagging and Bossting

Lecture 8: Section FAQ – Other algorithm of decision tree

Lecture 9: Introduction to Gradient Boosting

Lecture 10: FAQ – For other algorithm of decision tree

Lecture 11: Section PDF

Lecture 12: Bonus topic – Decision Tree using Python

Lecture 13: Bonus Topic – Analytics / Data Science / Machine Learning Interview questions

Lecture 14: Closure Note

Instructors

Gopal Prasad Malakar
Trains Industry Practices on data science / machine learning

Rating Distribution

1 stars: 11 votes

2 stars: 17 votes

3 stars: 53 votes

4 stars: 102 votes

5 stars: 116 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!