R: Design & Evaluate Random Forests for Attrition

位教师：EDUCBA

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2个模块

深入了解一个主题并学习基础知识。

4 小时完成

灵活的计划

自行安排学习进度

2个模块

深入了解一个主题并学习基础知识。

4 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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.

您将获得的技能

要了解的详细信息

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作业

6 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有2个模块

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.

In the second module, students construct, tune, and evaluate Random Forest models using real-world HR data. Through practical lessons, participants will apply parameter optimization techniques, analyze model performance using appropriate metrics, and justify their modeling choices using validation strategies. By the end of the course, learners will have the capability to build robust, interpretable machine learning models for workforce analytics and make informed data-driven decisions regarding employee retention.

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.

涵盖的内容

7个视频3个作业

7个视频总计54分钟

Introduction to Employee Attrition Prediction Using Random Forest 3分钟
Random Forest Overview 8分钟
Random Forest Overview Continue 11分钟
Variable Explanation 12分钟
Variable Explanation Continue 7分钟
Pre Modelling Steps 8分钟
Pre Modelling Steps Continue 6分钟

3个作业总计50分钟

Understanding the Problem and Technique 10分钟
Exploring and Preparing the Data 10分钟
Foundations of Employee Attrition Prediction 30分钟

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.