Why is data labeling important in supervised learning?

Data labeling is a critical step in supervised learning because it provides the necessary information for the model to learn from examples. In supervised learning, algorithms are trained on labeled datasets, where each training example is paired with an output label. This labeling process serves as a guide for the model, helping it understand the relationship between input data and the corresponding desired outcomes.

When the model processes a feature set, the labels it has been trained on allow it to recognize patterns and make predictions for new, unseen data. The quality and accuracy of the labels significantly influence the model's ability to generalize and perform well in real-world scenarios. Therefore, proper data labeling is essential for developing effective machine learning models.