What is the difference between supervised and unsupervised learning?

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Supervised and unsupervised learning are two core types of machine learning, differing primarily in how the model learns from data:

Supervised Learning:

• Definition: The model is trained on labeled data—each input has a corresponding output (target).

• Goal: Learn a mapping from inputs to known outputs.

• Examples:

  • Classification (e.g., spam vs. non-spam emails)

  • Regression (e.g., predicting house prices)

• Common Algorithms:

  • Linear Regression

  • Decision Trees

  • Support Vector Machines (SVM)

  • Neural Networks

• Use Cases: Fraud detection, customer churn prediction, medical diagnosis.

Unsupervised Learning:

• Definition: The model is trained on unlabeled data—there are no target outputs.

• Goal: Discover hidden patterns, groupings, or structures within the data.

• Examples:

  • Clustering (e.g., customer segmentation)

  • Dimensionality Reduction (e.g., PCA for visualization)

• Common Algorithms:

  • K-Means

  • Hierarchical Clustering

  • DBSCAN

  • Autoencoders

• Use Cases: Market research, anomaly detection, recommendation systems.

Key Difference:

• Supervised learning needs prior knowledge (labels) and makes predictions.

• Unsupervised learning explores data without labels to find patterns.

In short, supervised learning answers "What is this?", while unsupervised learning asks "What can I learn from this data?" without predefined outcomes.

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