About Course
Overview
The Decision Tree Modeling Using R Certification Training course is designed to help learners master one of the most widely used machine learning techniques — decision trees. Decision trees are powerful for both classification and regression problems, offering an intuitive way to visualize and interpret complex datasets.
This course focuses on using R programming to build, analyze, and optimize decision tree models. Learners will gain hands-on experience with R libraries, understand model evaluation techniques, and explore advanced methods such as pruning, random forests, and ensemble learning.
Whether you are a beginner in machine learning or a data professional seeking practical expertise in decision tree modeling, this program equips you with the skills to apply decision trees in business, finance, healthcare, marketing, and more.
Course Details
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Duration: 4–6 weeks (self-paced or instructor-led)
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Learning Mode: Online / Classroom / Blended
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Prerequisites:
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Basic knowledge of statistics and probability
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Familiarity with R programming fundamentals
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Understanding of datasets and data manipulation
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Course Content
Module 1: Introduction to Decision Trees
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What are decision trees?
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Types of decision trees: Classification vs. Regression
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Advantages and limitations
Module 2: R Basics Refresher
Module 3: Building Decision Trees in R
Module 4: Model Evaluation and Validation
Module 5: Tree Pruning and Optimization
Module 6: Ensemble Methods
Module 7: Real-World Applications
Module 8: Capstone Project
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