A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)
Logistic regression models the probability of a binary outcome by applying the logistic (sigmoid) function to a linear combination of predictors, bounding output between 0 and 1. This directly suits the churn yes/no target, unlike ordinary linear regression.
Why this answer
Logistic regression (C) is correct because it is a supervised classification technique that models the probability of a binary outcome (churn yes/no) using a sigmoid function, making it a standard choice for binary targets. Decision trees (D) are also correct because they are supervised classifiers that recursively split features to predict categorical class labels, and they handle binary targets naturally while offering interpretability. K-means clustering (A) is wrong because it is an unsupervised algorithm that groups unlabeled data and cannot predict a labeled binary target.
Linear regression (B) is wrong because it predicts continuous numeric values rather than class probabilities or discrete categories, so it is unsuitable for binary classification. The Apriori algorithm (E) is wrong because it is an unsupervised association-rule mining method for finding frequent itemsets, not a predictive classification model.
Exam trap
The trap here is that candidates may confuse unsupervised clustering (K-means) or association rule mining (Apriori) with supervised classification, or mistakenly think linear regression can be adapted for binary outcomes without transformation.