Which model is trained to assign a class to a sample based on the sample's features?

• A. Support vector machine
• B. Decision tree
• C. Random forest
• D. Naive Bayes

Answer: (A)

Classifying a sample by its features is the job of a classifier, and the support vector machine is a classic example designed specifically for this purpose. It learns a boundary that separates different classes in the feature space and assigns a class label to new samples based on which side of that boundary they fall on. During training, it aims to maximize the margin between the classes, which helps with better generalization to unseen data when making predictions.

The other methods also produce class labels but work differently. A decision tree splits the space with simple rules and assigns the label at a leaf. A random forest builds many trees and combines their outputs for a final label. Naive Bayes uses probability calculations to pick the most likely class given the features, using independence assumptions to simplify the math. The key idea here is that the model in question is a classifier that maps feature vectors directly to class labels, and the support vector machine is a primary example of that approach.