What is the difference between supervised and unsupervised learning?

The distinction between supervised and unsupervised learning primarily revolves around the nature of the data used in the training process. Supervised learning relies on labeled data, meaning that each training example is paired with a corresponding output or label. This allows the model to learn a mapping from inputs to outputs, making it well-suited for tasks such as classification and regression, where the goal is to predict outcomes based on input features.

In contrast, unsupervised learning does not use labeled data. Instead, it involves analyzing and clustering data without predefined categories or labels. This approach is utilized to discover patterns, groupings, or structures within the data based on the inherent characteristics of the input features. Common applications of unsupervised learning include clustering, anomaly detection, and dimensionality reduction.

The other statements do not accurately describe the core differences between supervised and unsupervised learning. While processing power requirements and speed can vary depending on the specific algorithms used rather than the learning type, the defining characteristic remains the nature of the data and the method of learning and pattern recognition employed by the algorithms.