What type of ML model is designed to be trained on labeled or unlabeled datasets?

• A. Decision Trees
• B. Neural Networks
• C. Support Vector Machines
• D. K-Means

Answer: (B)

Neural networks are versatile enough to learn from both labeled and unlabeled data. In supervised learning, they use labeled examples to learn mappings from inputs to known outputs. In unsupervised learning, they can work with unlabeled data to discover structure or learn representations—think autoencoders or other self-supervised techniques that train without explicit labels. This dual capability lets neural networks handle datasets with or without labels, which is why they’re the appropriate choice for a model designed to be trained on either type of data. By contrast, many traditional models like decision trees and support vector machines are typically trained on labeled data, while K-Means operates purely on unlabeled data.