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    Decision Tree – Theory, Application and Modeling using R

    adminBy adminAugust 26, 2022Updated:August 28, 2022No Comments1 Min Read
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    Decision Tree - Theory, Application and Modeling using R - Online Course Download
    Decision Tree - Theory, Application and Modeling using R - Online Course Download
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    Data Science Development free online course free udemy paid course freecourse freecoursesite R udemy course download udemy courses free download

    What you'll 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

     

    Requirements :

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

    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

    Who this course is for :

    Data Mining professionals
    Analytics professionals
    People seeking job in analytics industry

    Course Size Details :

    5.5 hours on-demand video
    7 articles
    18 downloadable resources
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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