True or false: Logistic regression is a categorical data mining model.

• A. False
• B. True
• C. Partially True
• D. Not Applicable

Answer: (B)

Predicting categorical labels is the goal here. Logistic regression is used to model the probability that an observation belongs to a particular category, typically the positive class in binary classification. It does this by applying the logistic function to a linear combination of input features, turning those features into a probability between 0 and 1, which is then mapped to a category. While the term “regression” appears in the name, the target is not a continuous value but a category, making it a classification (categorical data mining) technique. It’s also extendable to multiple categories through multinomial logistic regression, which handles more than two classes. This combination—predicting categorical outcomes and delivering class probabilities—is why logistic regression is considered a categorical data mining model.