This course guides learners through the structured development of predictive models using Random Forest techniques in R, specifically applied to employee attrition data. The course is divided into two comprehensive modules. The first module introduces the foundational concepts of classification and Random Forest algorithms, guiding learners to explain, identify, and prepare relevant variables. Learners also perform essential preprocessing tasks to shape the dataset for analysis.
R: Design & Evaluate Random Forests for Attrition
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3 hours to complete
Flexible schedule
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What you'll learn
Build and tune Random Forest models in R for real-world HR attrition datasets.
Apply preprocessing and variable selection for accurate employee attrition modeling.
Evaluate and validate model performance using metrics and optimization strategies.
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Recently updated!
September 2025
Assessments
6 assignments
Taught in English
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There are 2 modules in this course
This module introduces learners to the fundamentals of employee attrition prediction using Random Forest algorithms in R. It begins with an overview of the business problem, explores the machine learning methodology behind Random Forest, and establishes a strong conceptual framework. Learners will also examine the structure and significance of the dataset, understand variable types and transformations, and perform essential pre-modeling tasks such as data cleaning and encoding. By the end of this module, learners will be able to prepare data and understand Random Forest fundamentals essential for building predictive models.
What's included
7 videos3 assignments
This module focuses on implementing, tuning, and validating Random Forest models for employee attrition prediction. Learners will begin by developing a predictive model using cleaned and preprocessed data. They will then explore techniques to optimize model performance, including parameter tuning and validation methods. Emphasis is placed on understanding how hyperparameters influence model behavior and ensuring robust evaluation using appropriate metrics. By the end of the module, learners will be able to build, fine-tune, and validate a Random Forest model that generalizes well to unseen data.
What's included
5 videos3 assignments
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