Recode Hive
Machine Learning #592
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What are the key differences between supervised and unsupervised learning? |
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Supervised Learning: Uses labeled data (input-output pairs). Uses unlabeled data (no predefined outputs). |
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Supervised Learning:
Uses labeled data (input-output pairs).
The model learns from past data and makes predictions.
Examples: Regression, Classification (e.g., Spam detection, Face recognition).
Algorithms: Linear Regression, Decision Trees, Support Vector Machines (SVM), Neural Networks.
Unsupervised Learning:
Uses unlabeled data (no predefined outputs).
The model finds hidden patterns and relationships in data.
Examples: Clustering, Anomaly Detection (e.g., Customer segmentation, Fraud detection).
Algorithms: K-Means, DBSCAN, PCA, Autoencoders.