What is the difference between supervised and unsupervised learning?

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Supervised Learning and Unsupervised Learning are two main types of machine learning, differing in how the model learns from data.

1. Supervised Learning

• Definition: The model is trained on labeled data, meaning each input has a known output.

• Goal: Learn a mapping from inputs to outputs to make predictions.

• Examples:

  • Classification (spam or not spam)

  • Regression (predicting house prices)

• How it works:

  • Feed the model with (input, output) pairs.

  • The algorithm adjusts itself to minimize prediction error.

• Algorithms: Linear Regression, Decision Trees, SVM, Neural Networks.

2. Unsupervised Learning

• Definition: The model is trained on unlabeled data—no predefined outputs.

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

• Examples:

  • Clustering (grouping customers by buying behavior)

  • Dimensionality reduction (PCA for feature reduction)

• How it works:

  • The algorithm looks for similarities or patterns without guidance.

• Algorithms: K-Means, Hierarchical Clustering, DBSCAN, PCA.

Summary:

• Supervised: Needs labeled data, predicts known outcomes.

• Unsupervised: Works on unlabeled data, finds hidden structures.

Read More :

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