The logistic regression is a(n) ______ model.

• A. Classification
• B. Prediction
• C. Clustering
• D. Regression

Answer: (B)

Logistic regression is designed to estimate the probability that an observation belongs to the positive class given its features. It uses the logistic function to map a linear combination of inputs to a value between 0 and 1, which is the predicted probability P(Y=1|X). That probabilistic output is what you’re actually predicting for new data, and you can convert it into a class decision by applying a threshold. In that sense, its primary role is to generate predictions—probabilities that inform decisions—about the outcome, rather than directly predicting a continuous numeric value or discovering group structure. While it is commonly used for binary classification, the defining trait here is producing a predicted probability, which aligns with the idea of a predictive model. Clustering is unsupervised and has no target outcome, and regression would imply predicting a continuous target, which logistic regression does not; it targets a binary outcome through probability-based prediction.