A Proposed Data Analytics Workflow and Example Using the R Caret Package

This paper provides a comprehensive explanation of functions that are available in the R caret package, and proposed workflow in how one might use them to perform predictive modeling. There is a void in demonstrating the... [ view full abstract ]

This paper provides a comprehensive explanation of functions that are available in the R caret package, and proposed workflow in how one might use them to perform predictive modeling. There is a void in demonstrating the functional components, expected runtime, and how an analyst might use those in a typical workflow for data mining and predictive analytics tasks. We attempt to fill this void by providing the reader a valuable reference in how to get started using caret by motivating the functions and their use with a real-world dataset. The sample dataset we analyzed was acquired from the 2017 WSDM-KKBox’s Churn Prediction Challenge on Kaggle.com, a platform for predictive analytics and data mining competitions. We demonstrate ways to clean, impute, pre-process, train, and assess popular machine learning models using caret. However, our contribution focuses on the actual process of getting results, as opposed to the results (e.g. model accuracy) themselves.