Decision Tree - Theory, Application and Modeling using R

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Decision Tree – Theory, Application and Modeling using R. Analytics (objective segmentation): Learn Data Science (applied statistics) CHAID / CART / GINI/ ID3/ Random Forest etc.

40,00  10,00 

The name of this course is Decision Tree – Theory, Application and Modeling using R. The knowledge you will get with this indescribable online course is astonishing. Analytics (objective segmentation): Learn Data Science (applied statistics) CHAID / CART / GINI/ ID3/ Random Forest etc..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Gopal Prasad Malakar, one of the very best experts in this field.

Description of this course: Decision Tree – Theory, Application and Modeling using R

Course Description What is this course? Decision Tree Model building is one of the most applied technique in analytics enhiesto. 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 Coche pruning technique Understand R procedure for coche 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 of this course: Decision Tree – Theory, Application and Modeling using R

What are the requirements? The course is fairly simple but it will help if they understand how to read excel formula

What will you learn in this course: Decision Tree – Theory, Application and Modeling using R?

What am I going to get from this course? 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, coche validation and implementation of decision tree

Target audience of this course: Decision Tree – Theory, Application and Modeling using R

Who is the target audience? Data Mining professionals Analytics professionals People seeking job in analytics industry