What is the goal of Supervised Learning in machine learning?

The goal of Supervised Learning in machine learning is to learn from labelled datasets. In this approach, an algorithm is trained using a dataset that contains input-output pairs, where the output is the label that accompanies each input. This allows the model to identify patterns and relationships within the data that can be generalized to make predictions on new, unseen data.

By utilizing this method, the model effectively learns to associate specific features in the data with particular outcomes, enhancing its predictive capabilities. This is crucial for tasks like classification and regression, where the objective is to accurately predict outcomes based on the learned patterns.

Supervised Learning stands in contrast to methods that operate without pre-defined labels (unlabelled datasets), which is the approach taken in unsupervised learning. Thus, the focus on leveraging labelled datasets is essential for the successful application of Supervised Learning to real-world problems.