Introduction to Classification in R

4 hours of instruction

Classification is a machine learning technique that can be used to sort novel data into labeled categories. In this course, learners will identify use cases for classification algorithms and become familiar with the theoretical underpinnings of supervised machine learning (working with labeled data). In particular, learners will build, evaluate, and interpret a k-nearest neighbors model in R, based on one of the most commonly used classification algorithms.

OBJECTIVES

1. Identify opportunities and use cases for predictive analytics
2. Build kNN classification model to anticipate events and behaviors
3. Evaluate accuracy of kNN algorithm

PREREQUISITES

Learners must be comfortable using R to manipulate data and must know how to create basic visualizations.

SYLLABUS & TOPICS COVERED

1. kNN
   • Classification use cases
   • kNN algorithm in a nutshell
   • Implement kNN on a dataset

SOFTWARE REQUIREMENTS

You will have access to an R-based Posit Cloud environment for this course. No additional download or installation is required.