What is supervised learning?

Supervised learning is a fundamental approach in machine learning where a model is trained on labeled data. This means that during the training phase, the dataset used contains both the input data and the corresponding correct output or label. The purpose of using labeled data is to enable the model to learn the relationship between the inputs and outputs. As the model processes this data, it identifies patterns and features that correlate the inputs to their respective outputs, which allows it to make accurate predictions on new, unseen data.

The effectiveness of supervised learning heavily relies on the quality and quantity of the labeled data provided during training. By using these labels, the model can evaluate its predictions and adjust accordingly to minimize errors. This process typically involves algorithms such as linear regression, decision trees, or neural networks, which leverage the labeled dataset to refine their predictions.

The other options describe different aspects of learning methods. For instance, one option refers to machine learning without feedback, which aligns more with unsupervised learning. Another option discusses models trained on unlabeled data, which is again characteristic of unsupervised learning. Lastly, while human intervention can be important in many contexts, supervised learning specifically denotes the usage of labeled data for model training, making it distinct from other types of learning scenarios.