What is the difference between supervised and unsupervised machine learning?
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Model Answer & Deep-Dive Explanation
Supervised learning uses labeled training data, where each input has a known output. The model learns the relationship between inputs and outputs to make predictions on new data. Common examples include classification and regression.
Unsupervised learning uses unlabeled data to discover hidden patterns, groups, or relationships without predefined target outputs. Common examples include clustering and dimensionality reduction.
Examples: - Supervised: Predicting house prices using historical data with known prices. - Unsupervised: Grouping customers based on purchasing behavior.
Key difference: Supervised learning predicts known target values, while unsupervised learning discovers patterns in data without labeled targets.
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