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Difference between Supervised vs Unsupervised vs Reinforcement learning

Learning TypeSupervised LearningUnsupervised LearningReinforcement Learning
Training DataLabeled data (input & output pairs)Unlabeled data (only input)Feedback from environment
Learning ObjectiveTo learn a mapping function from input to outputTo discover patterns and relationships in dataTo maximize cumulative reward
Example Use CasesImage classification, Regression, Speech recognitionClustering, Dimensionality reductionGame playing, Robotics
Teacher/GuideProvided with correct answers or labelsNo explicit teacher or guideReward signal from environment
Model OutputPredictions based on learned patternsCluster/group data, Dimension-reduced representationActions to take in an environment
Evaluation MetricTypically uses metrics like accuracy, lossQuality of clustering, Reconstruction errorCumulative reward, Success rate
Approach ComplexityOften simpler as it has labeled data for direct comparisonMore complex as it requires finding patterns in dataComplex due to the interaction with the environment
Key ChallengesRequires labeled data, may suffer from overfittingDifficulty in identifying the correct clustering or patternsBalancing exploration and exploitation