Decision Tree Modeling Using R
- Offered byThe knowledge academy
Decision Tree Modeling Using R at The knowledge academy Overview
Duration | 1 day |
Start from | Start Now |
Total fee | ₹19,995 |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
- Admission Process
Decision Tree Modeling Using R at The knowledge academy Highlights
- Earn a certificate after completion of course
- Engage in activities, and communicate with your trainer and peers
Decision Tree Modeling Using R at The knowledge academy Course details
Data Scientists
Machine Learning Engineers
Data Analysts
Research Scientists
Quantitative Researchers
Risk Assessment Managers
Predictive Modelers
To Understand the fundamentals of Decision Tree Modelling
To Learn data treatment and frequency distribution techniques
To Explore Decision Tree algorithm development and pruning
To Gain expertise in advanced topics like Random Forest and CHAID Algorithm
To Acquire practical skills in using R for Decision Tree Modelling
To Become proficient in applying Decision Tree Modelling to real-world scenarios
Decision Tree Modelling Using R is a formidable analytical technique with wide-ranging applications across diverse industries in India, including finance, automotive, and telecommunications
It serves as a powerful tool for making data-driven decisions, enabling businesses to navigate complex scenarios and optimize outcomes
Proficiency in Decision Tree Modelling Using R is essential for professionals in India seeking to enhance their data science skills and make informed decisions rooted in data analysis
Decision Tree Modeling Using R at The knowledge academy Curriculum
Module 1: Introduction to Decision Tree
Decision Tree Modelling Objective
Anatomy of a Decision Tree
Important Terminology Related to Decision Trees
Module 2: Overview of R Programming
R Programming Language
Data Types
Control Structures in R
Module 3: Data Treatment Before Modelling
Data Sanity Check-Contents
View
Frequency Distribution
Uni-Variate
Categorical Variable Treatment
Module 4: Classification of Tree Development and Algorithm Details
Installing R Package and R studio
Developing First Decision Tree in R Studio
Find Strength of the Model
Module 5: Decision Tree Analysis in Project Management
Use Decision Tree in Project Management
Tools for Decision Tree Analysis
Decision Tree Analysis Strategy
Module 6: Regression Tree and Auto Pruning
Introduction to Pruning
Understand K Fold Validation for Model
Develop Regression Tree
How is it Different from Linear Regression
Advantages and Disadvantages over Linear Regression
Module 7: CHAID Algorithm
What is CHAID/CART Algorithm
Chi-Square Statistics
Implement Chi-Square for Decision Tree Development
CHAID Vs CART
Module 8: Other Algorithms
ID3
Random Forest Method
Using R for Random Forest Method
Decision Tree Modeling Using R at The knowledge academy Admission Process
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