How does supervised learning differ from unsupervised learning?.

Supervised learning involves training a model on a labeled dataset, meaning that each training example is paired with an output label. This approach is often used for classification and regression tasks. In contrast, unsupervised learning deals with unlabeled data, where the algorithm tries to learn the underlying structure or distribution of the data without specific output labels. Common applications include clustering and dimensionality reduction.

Key Facts

• Supervised learning requires labeled data for model training.
• Unsupervised learning does not use labeled data.
• Supervised learning is typically more accurate due to its reliance on known outputs.
• Unsupervised learning can discover hidden patterns in data.

Examples or Use Cases

• Supervised Learning: Email spam detection, image classification.
• Unsupervised Learning: Customer segmentation, anomaly detection.

FAQs

• What is a labeled dataset? A dataset where each input has a corresponding correct output.
• Can unsupervised learning be used for prediction? Generally, unsupervised learning is used for exploration, not direct prediction.