Despite its name, logistic regression is a supervised learning algorithm primarily used for classification, not regression. It predicts a categorical target variable with two possible outcomes (binary classification). These outcomes are typically represented as binary values (e.g., 0 or 1, true or false, yes or no).
Examples of classification problems include:
- Identifying fraudulent transactions (fraudulent or not fraudulent)
- Classifying images of animals (cat, dog, bird, etc.)
- Diagnosing diseases based on patient symptoms (disease present or not present)