Decision Tree Modeling Using R Certification Training

Establish proficiency in developing Decision Tree using R programming language

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Key Highlights

  • Self-paced
  • Practical Assignments
  • Lifetime Access to Learning Management System
  • 24x7 Expert Support
  • Course Completion Certificate
  • Online Forum for Discussions

Course Price

$99.00 $199.00

Contact Us

1866-216-7898

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Available Courses Delivery

This course is available in the following formats:

Self Paced (On-Demand)

24x7 access to instructor-led videos and practical activities
Convenient training that syncs with your schedule
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$199  $99

Course Overview

This course has been designed to train learners in various concepts, such as, Data Design, Regression Tree, and Pruning. It teaches about the numerous benefits of Decision Tree. It introduces trainees to several algorithms that work behind decision tree.

Course Objectives

  • Introduction to Decision Tree
  • Educate about procedure of applying Decision Tree
  • Teach about benefits of Decision Tree
  • Train in numerous algorithms behind decision tree
  • Familiarize with interpreting the Decision Tree output of R programming language
  • Acquaint learners with the steps to develop Decision Tree in R programming language

Career Benefits

  • Great remuneration as an expert in Decision Tree Modeling
  • Demonstrate expertise in Decision Tree development using R programming language
  • Multi-industry opportunities

Prerequisites

  • Prior knowledge of R programming language
  • Basic knowledge of Excel formulas

Who should take up?

  • Analytics Professionals
  • Data Mining Professionals
  • Professionals and Students who want to enter Analytics Industry

Course Content

  • Decision Tree modeling Objective
  • Anatomy of a Decision Tree
  • Gains from a decision tree (KS calculations)
  • Definitions related to objective segmentations
  • Historical window
  • Performance window
  • Decide performance window horizon using Vintage analysis
  • General precautions related to data design
  • Data sanity check-Contents
  • View
  • Frequency Distribution
  • Means / Uni-variate
  • Categorical variable treatment
  • Missing value treatment guideline
  • Capping guideline
  • Preamble to data
  • Installing R package and R studio
  • Developing first Decision Tree in R studio
  • Find strength of the model
  • Algorithm behind Decision Tree
  • How is a Decision Tree Developed?
  • First on Categorical dependent variable
  • GINI Method
  • Steps taken by software programs to learn the classification (develop the tree)
  • Assignment on decision tree
  • Discussion on assignment
  • Find Strength of the model
  • Steps taken by software program to implement the learning on unseen data
  • Learning more from practical point of view, Model Validation and Deployment
  • Introduction to Pruning
  • Steps of Pruning
  • Logic of pruning
  • Understand K fold validation for model
  • Implement Auto Pruning using R
  • Develop Regression Tree
  • Interpret the output
  • How it is different from Linear Regression
  • Advantages and Disadvantages over Linear Regression
  • Another Regression Tree using R
  • Key features of CART
  • Chi square statistics
  • Implement Chi square for decision tree development
  • Syntax for CHAID using R, and CHAID vs CART
  • Entropy in the context of decision tree
  • ID3
  • Random Forest Method and Using R for Random forest method

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