Supervised Learning is a machine learning paradigm where models are trained using labeled examples. Each training instance comes with input features and an associated correct output (label), enabling the model to learn the relationship between inputs and outputs. Over time, the model generalizes these patterns to make predictions on new, unseen data. Common supervised learning tasks include classification (assigning a category) and regression (predicting a continuous value).
How It Works:
- Labeled Data: The algorithm is provided with pairs of input data and corresponding correct labels.
- Model Training: By adjusting model parameters to minimize the difference between its predictions and the known labels, the model “learns” to map inputs to outputs.
- Testing and Validation: Once trained, the model’s performance is evaluated on separate datasets to ensure it can accurately predict labels for unseen data.
Why It Matters:
Supervised Learning is foundational in machine learning, driving applications like spam filtering, image recognition, and medical diagnosis. By learning from labeled data, models can make informed predictions that power decision-making, automation, and innovation across industries.
