Supervised Learning is a type of machine learning that explicitly requires splitting the data into training and testing datasets before running an algorithm against that data. In supervised learning, the algorithm is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns from this labeled data and makes predictions or decisions based on the input data. The testing dataset, which is separate from the training dataset, is used to evaluate the model's performance and ensure it generalizes well to new, unseen data.

References: Dell Information Storage and Management V5 course material -Dell Education