Understanding Supervised Learning and its Application in Machine Learning

In the field of machine learning, supervised learning is a type of learning where a model is trained on a labeled dataset that contains input-output pairs. The goal is for the model to learn the mapping between inputs and outputs so that it can make predictions on unseen data.

One common example of supervised learning is the classification task. In this task, the model is trained to classify input data into predefined categories based on the labeled examples it has seen during the training phase. A well-known application of supervised learning in this context is the classification of emails into spam or non-spam categories.

Overall, supervised learning is a fundamental concept in machine learning that plays a crucial role in building predictive models for various applications.