Supervised Learning
A fundamental machine learning paradigm where an algorithm is trained on a dataset containing strict pairs of inputs and their perfectly correct outputs.
Think of It Like This
Like a student doing math homework with the teacher's answer key sitting right next to them, constantly checking if they got the problem right.
The model learns to map the input features to the target labels by minimizing the error (loss) between its predictions and the ground truth. It is the dominant approach for tasks like image classification, spam detection, and medical diagnosis. Its primary limitation is the massive cost of acquiring high-quality, human-labeled data.