Supervised Learning
A machine learning approach where the model is trained on labeled data with known correct answers.
Definition
Supervised learning is a machine learning approach where a model is trained on a dataset of input-output pairs — examples where the correct answer is already known. The model learns to map inputs to outputs by analyzing thousands or millions of labeled examples, then applies those learned patterns to predict answers for new, unseen inputs.
Common supervised learning tasks include classification (is this email spam or not?), regression (what will this house sell for?), and prediction (will this customer churn?). The "supervised" label refers to the human supervision involved in labeling the training data.
Why It Matters
Supervised learning powers many of the AI applications businesses encounter daily — spam filters, recommendation engines, fraud detection, and predictive analytics. Understanding this approach helps professionals evaluate AI tools and identify opportunities to apply machine learning to business problems.
For organizations considering AI adoption, supervised learning is often the most straightforward path to value. If you have historical data with known outcomes (sales that closed, support tickets that escalated, campaigns that performed well), supervised learning can identify the patterns that predict future outcomes.
Examples in Practice
An e-commerce company trains a supervised learning model on five years of customer purchase data to predict which customers are likely to churn, enabling proactive retention campaigns that reduce churn by 20%.
A financial institution uses supervised learning on millions of labeled transactions (fraudulent vs. legitimate) to build a fraud detection system that catches 95% of fraudulent transactions in real-time.
A marketing team trains a model on their historical email campaigns, learning which subject lines, send times, and content types correlate with high open and click rates, then uses those insights to optimize future campaigns.