What is the role of a target variable in supervised learning?

In supervised learning, the target variable is crucial because it is the outcome or response that the model aims to predict. It contains the desired output or class labels that guide the learning process. When a model is trained, it learns to associate inputs (also known as independent variables or features) with the corresponding target variable values. This relationship enables the model to make predictions on new, unseen data based on the patterns it learned from the training dataset.

Emphasizing the importance of the target variable, during the training phase, the algorithm adjusts its parameters to minimize the error between its predictions and the actual values of the target variable across the training dataset. For instance, in a classification task, the target variable would contain the specific classes that each input belongs to, while in regression tasks, it would hold continuous values that the model seeks to estimate.

In summary, the target variable serves as the benchmark for the model's performance, making it indispensable for supervised learning, where the primary goal is to predict results based on learned relationships from the training data.