What is the primary difference between supervised and unsupervised machine learning?

Correct answer: Supervised learning uses labeled data for training, while unsupervised learning finds patterns in unlabeled data

Option D is correct because supervised learning trains models on labeled datasets where each example has a known output, enabling the model to learn a mapping from inputs to outputs, whereas unsupervised learning discovers hidden patterns or groupings in data that has no predefined labels. Option A is incorrect because computational resource requirements depend on model architecture and dataset size, not on whether the learning is supervised or unsupervised. Option B is incorrect because training speed depends on algorithm complexity and data volume, not the supervision paradigm. Option C is incorrect because supervised learning encompasses both regression (numerical prediction) and classification (categorical prediction), so it is not limited to numerical values.