Decision Tree Modeling Using R Certification Training

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Enroll now to become a Decision Tree Modeling expert with CertZip Decision Tree Modeling Using R Certification Training, upgrade your skills, and lead your professional life.

Course Description

This course is designed to understand Decision Tree Modeling Using the R platform by mastering concepts like Data design, Regression Tree, Pruning, and various algorithms like CHAID, CART, ID3, GINI, and Random forest.

Decision Tree Model. Decision tree analysis involves making a tree-shaped diagram or statistical probability analysis to chart a course of action. It is utilized to break down complex issues or branches. Each branch of the decision tree could be a viable outcome.

Decision trees help you to evaluate your options. Decision Trees are beautiful tools for selecting between several methods of action. They deliver a highly effective structure within which you can lay out opportunities and explore the potential consequences of selecting those options.

It is one way to display an algorithm containing only conditional control statements. Decision trees are commonly used in operations research, specifically decision analysis, to help identify a likely strategy to reach a goal. Still, they are also a popular tool in machine learning.

A decision tree is a graph representing choices and their results in the form of a tree. The nodes in the graph represent an event or choice, and the graph's edges represent the decision rules or conditions. It is mainly used in Machine Learning and Data Mining applications using R.

A tree has many analogies in real life, and it turns out that it has influenced a wide area of machine learning, covering both classification and regression. A decision tree can visually and explicitly represent decisions and decision-making in decision analysis.

Variable preselection: Additional tests can be done like multicollinearity test, VIF calculation, IV calculation on variables to select only a few top variables. ... Ensemble Learning Utilize multiple trees (random forests) to indicate the outcomes.

What you'll learn

  • In this course, you will learn: Data design, Regression Tree, Pruning and various algorithms like CHAID, CART, ID3, GINI, and more

Requirements

  • There is no requirement to pursue this Certification.

Curriculam

In this module, you will understand What is a Decision Tree and what are the benefits. What are the core objectives of Decision Tree modelling, How to understand the gains from the Decision Tree and How does one apply the same in business scenarios

Decision Tree modeling Objective
Anatomy of a Decision Tree
Gains from a decision tree (KS calculations)
Definitions related to objective segmentations

n this module, you will learn how to design the data for modelling

Historical window,
Performance window,
Decide performance window horizon using Vintage analysis,
General precautions related to data design

In this module, you will learn how to ensure Data Sanity check and you will also learn to perform the necessary checks before modelling

Data sanity check-Contents
View
Frequency Distribution
Means / Uni-variate
Categorical variable treatment
Missing value treatment guidelines
capping guideline

In this module, you will learn to use R and the Algorithm to develop the Decision Tree.

The Preamble to data
Installing The R package and R studio
Developing first Decision Tree in R studio, Creating the first Decision Tree in R studio
Find the Strength of the model
The algorithm behind Decision Tree
How is a Decision Tree grown?
First on Categorical dependent variable, First, on the Categorical dependent variable
GINI Method
Actions taken by software programs to understand the classification
Assignment on decision tree

In this module you will understand how Classification trees are Developed, Validated and Used in the industry

Discussion on assignment
Find The Strength of the model
Steps were taken by the software program to execute the learning on unrecognized data,
learning better from a practical point of view
Model Validation and Deployment

In this module you will understand the Advance stopping criteria of a decision tree. You will also learn to develop Decision Trees for numerous outcomes.

Introduction to Pruning,
Steps of Pruning,
Logic of pruning,
Understand K fold validation for the model,
Implement Auto Pruning using R,
Develop a Regression Tree,
Interpret the output,
How it is different from Linear Regression,
Advantages and Disadvantages of Linear Regression,
Another Regression Tree using R

In this module you will learn what is Chi square and CHAID and their working and also the difference between CHAID and CART etc..

Key features of CART,
Chi square statistics,
Implement Chi square for decision tree development,
Syntax for CHAID using R,
CHAID vs. CART

know about ID3, Entropy, Random Forest, and Random Forest using R

Entropy in the context of the decision tree
ID3, Random Forest Method and Using R for Random forest method
Project

FAQ

Simple to understand and interpret. Requires little data preparation. The cost of using the tree is logarithmic in the number of data points utilized to train the tree. Able to control both numerical and categorical data. Able to handle multi-output problems.

To better understand the Decision Tree Modeling Certification Training, one must learn as per the curriculum.

Decision tree understanding is a process typically utilized in data mining. The purpose is to make a model that indicates the value of a target variable established on several input variables. A decision tree is a straightforward representation for classifying examples.

Decision Tree is so famous for Bagging Machine Learning that it has its Random Forest package. Random Forest sounds like a large group of trees, and it is indeed built from several Decision Tree models, 100 models by default, from sub-samples of the training dataset.

One of the powers of a decision tree model is that it creates outcomes that are easy to comprehend in terms of the predictor and target variables. An induced rule set might be even better because it expresses the decision tree splits in terms of IF-THEN-ELSE rules, accessible for managers to understand.

CertZip Support Team is for a lifetime and will be open 24/7 to help with your questions during and after completing the Decision Tree Modeling Using R Certification Training.

By enrolling in Decision Tree Modeling Using R and completing the module, you can get the CertZip Decision Tree Modeling Using R Training Certification.

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Training Course Features

Assessments
Assessments

Every certification training session is followed by a quiz to assess your course learning.

Mock Tests
Mock Tests

The Mock Tests Are Arranged To Help You Prepare For The Certification Examination.

Lifetime Access
Lifetime Access

A lifetime access to LMS is provided where presentations, quizzes, installation guides & class recordings are available.

24x7 Expert Support
24x7 Expert Support

A 24x7 online support team is available to resolve all your technical queries, through a ticket-based tracking system.

Forum
Forum

For our learners, we have a community forum that further facilitates learning through peer interaction and knowledge sharing.

Certification
Certification

Successfully complete your final course project and CertZip will provide you with a completion certification.

Decision Tree Modeling Using R Certification Training

Decision Tree Modeling Using R Training demonstrates that the holder has the proficiency and aptitudes to work with Decision Tree.

A decision tree is a graph representing choices and their results in the form of a tree. The nodes in the graph represent an event or choice, and the graph's edges represent the decision rules or conditions. It is mainly used in Machine Learning and Data Mining applications using R.

Using a Decision Tree aims to create a training model that can predict the class or value of the target variable by learning simple decision rules inferred from prior data.

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