What is the difference between classification and clustering in machine learning?

Correct answer: Classification predicts predefined categories while clustering groups similar items without predefined labels

Option D is correct because classification is a supervised learning task where a model learns from labeled training data to predict discrete, predefined category labels for new inputs, whereas clustering is an unsupervised technique that groups data points by similarity without any prior label definitions. Option A is partially correct that classification is supervised, but the claim that clustering is always unsupervised is an overstatement, as semi-supervised clustering variants exist, making the absolute framing misleading. Option B is incorrect because clustering applies to both numerical and categorical data, with algorithms such as k-modes designed specifically for categorical features. Option C is incorrect because algorithm speed depends on implementation, dataset size, and dimensionality, not on whether the task is classification or clustering.