Logistic Regression in R

4 hours of instruction

Logistic regression is a classification algorithm useful for sorting data into two classes: either this, or that. In this course, learners will identify use cases for logistic regression in R. They will wrangle data and implement a logistic regression model before attempting to evaluate its effectiveness. Finally, learners will use their knowledge of the mathematics behind logistic regression to tune the model and improve its classificatory function.

OBJECTIVES

1. Identify opportunities and use cases for classification algorithms
2. Summarize the process and the math behind logistic regression
3. Assess results of classification model per formance
4. Tune the model using grid search cross- validation

PREREQUISITES

Learners must be comfortable using Python to manipulate data, must know how to create basic visualizations and having background on classification use cases is recommended.

SYLLABUS & TOPICS COVERED

1. Logistic Regression
   • Logistic regression use cases and logic behind it
   • Implementation of logistic regression on a dataset
   • Evaluation of the results and tuning the model

SOFTWARE REQUIREMENTS

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