Which type of machine learning provides known label values as input for future predictions?

Supervised learning is the correct type of machine learning that utilizes known label values as input for future predictions. In this approach, the model is trained on a labeled dataset, which means that each training example contains both the input features and the corresponding output label. The purpose of supervised learning is to learn a mapping from inputs to outputs based on this labeled data. Once the model has been trained, it can make predictions on new, unseen data by applying the learned mapping.

In contrast, unsupervised learning deals with data that has no labels, meaning the model tries to identify patterns or groupings from the input data alone without any guidance on expected outputs. Reinforcement learning focuses on learning through interactions with an environment to achieve a specific goals, using feedback in the form of rewards or penalties rather than labeled input data. Transfer learning involves taking a pre-trained model on one task and fine-tuning it for a related task, but it does not inherently rely on labeled data in the same way that supervised learning does. Understanding these distinctions highlights why supervised learning is specifically suited for scenarios where known label values are provided for model training and future predictions.