Definition
Supervised learning is a type of machine learning where an algorithm learns from a labeled dataset. This means each data point in the training set has a corresponding correct output or target variable. The algorithm's goal is to learn a mapping function that can predict the output for new, unseen data based on the patterns it identified during training. In practice, this involves feeding the model input features and their associated correct answers, allowing it to adjust its internal parameters until it can accurately predict outcomes. Understanding supervised learning is crucial for data scientists and analysts as it enables the creation of predictive models for tasks like classification and regression, directly impacting business decisions and operational efficiency. For instance, a supervised learning model can be trained on historical customer data to predict future purchasing behavior. This differs from unsupervised learning, which seeks to find patterns in unlabeled data. Supervised learning concepts are frequently assessed in AI and data science certifications, including AWS Certified Machine Learning Specialty.