Decision Tree - Theory, Application and Modeling using R
- Offered byUDEMY
Decision Tree - Theory, Application and Modeling using R at UDEMY Overview
Duration | 5 hours |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Go to Website  |
Credential | Certificate |
Decision Tree - Theory, Application and Modeling using R at UDEMY Course details
- Data Mining professionals
- Analytics professionals
- People seeking job in analytics industry
- 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
- 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
Decision Tree - Theory, Application and Modeling using R at UDEMY Curriculum
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
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