Find online courses made by experts from around the world.
Take your courses with you and learn anywhere, anytime.
Learn and practice real-world skills and achieve your goals.
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
Material in this course
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
Section 2 – practical (for categorical output)
Section 3 – Algorithm behind decision tree
Section 4 – Other algorithm for decision tree
Why take this course?
Take this course to
Not for you? No problem.
30 day money back guarantee.
Learn on the go.
Desktop, iOS and Android.
Certificate of completion.
|Section 1: Introduction to decision tree|
Need of a decision tree
Anatomy of a Decision Tree
Gain From a Decision Tree
KS of a decision tree
Business Application of a Decison treePreview
Defintions related with Objective segmentation
Decision Tree vs Logistic Regression
|Section 2: Demo of Decision Tree development using R|
Understand The data for Demo
View resource to download files
How to download excel files, R program etc?
Install R and R Studio
First Decision Tree in RPreview
Second Decision Tree in R
|Section 3: Algorithm behind decision tree|
Intutive Understanding of Numeric Variable Split
GINI Index of a nodePreview
GINI Index of a Split
CART in action : Decide which variable n its value for the split
Practical approach of Decision Tree Development
Implementing decision tree model
Auto Pruning Technique of decision tree development
K Fold Cross Validation
Auto Pruning Using R.
Developing Regression Tree Using R
Interpret Regression Tree Output
Another Regression Tree Using R
CHAID vs CART
Appendix Content - Chi Square Statistic
Appendix Content - Feel The Chi Square StatisticPreview
Appendix content - Degree of freedom of a cross tab
Appendix content - Chi Square Distribution
Appendix content - PDF
|Section 4: Other algorithm of decision tree development|
Entropy of a Node
Entropy of a Split
Random Forest MethodPreview
R syntax for Random Forest
I am a seasoned Analytics professional with 15+ years of professional experience. I have industry experience of impactful and actionable analytics. 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 and MS access based database application development.