Why is labeling important in supervised learning?

In supervised learning, labeling is crucial because it provides the model with the guidance it needs to make accurate predictions. The labels act as the target or output that the model aims to learn to predict based on the input features. By training on labeled data, the model develops a relationship between input variables and their corresponding outputs, effectively learning the underlying patterns in the data.

This process is fundamental because the labeled examples serve as ground truth during training, enabling the model to adjust its parameters and improve its performance iteratively. Without these labels, the model would lack the necessary reference points to understand what it should be predicting, resulting in ineffective training and poor performance when applied to new, unseen data.

This highlights the indispensable role that labels play in the learning process, as they are essential for teaching the model how to interpret and respond to various inputs effectively.