Supervised Learning

Overview

A machine learning paradigm where a model is trained on a labeled dataset, meaning that each input data point is paired with a corresponding target output or label. The model learns to map inputs to outputs by minimizing the difference between its predictions and the true labels, which is usually done by gradient descent methods. Supervised learning is commonly used for tasks such as classification and regression, and can be applied to many different problems when labeled data is available.

Use of Labeled Data

Supervised learning models use labeled data where each input has a corresponding target output.

Learning Process

The model learns by mapping the inputs to the outputs by minimizing the errors based on the labels.

Common Applications

This approach is common in many AI tasks including classification and regression problems.

Related Concepts

• Machine Learning
• Unsupervised Learning
• Deep Learning
• Model Training
• Model Training Dataset
• Loss Function