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Difference between Supervised vs Unsupervised vs Reinforcement learning

AspectSupervised LearningUnsupervised LearningReinforcement Learning
Learning ParadigmLabeled data with input-output pairsUnlabeled data with no explicit target labelsFeedback-based learning through interactions
Input DataInput features (X) and corresponding target labels (Y)Input features (X) without corresponding target labelsInput features (X) and environment feedback (rewards and penalties)
GoalMake predictions or decisions on new dataDiscover patterns and relationships in dataLearn a policy to make optimal decisions
Example ApplicationsImage classification, sentiment analysis, regression tasks, etc.Clustering, anomaly detection, dimensionality reduction, recommendation systems,Game playing (e.g., AlphaGo), robotic control, self-driving cars, etc.
Training ApproachSupervised learning algorithms optimize a mapping between X and Y using labeled dataUnsupervised learning algorithms seek to find patterns or structure in the data without labelsModel learns through trial and error with exploration and
Knowledge RequiredRequires labeled data for trainingDoes not require labelled dataRequires understanding of the environment and its feedback
EvaluationPerformance measured based on prediction accuracy or other classification metricsEvaluation is more challenging and may be based on metrics like clustering qualityEvaluation is based on long-term cumulative rewards and penalties
Exploration vs ExploitationNot applicableNot applicableBalancing exploration and exploitation
Common AlgorithmsLinear regression, logistic regression, support vector machines, decision trees, etc.K-Means clustering, Gaussian Mixture Models, autoencoders, etc.Q-learning, Deep Q Network (DQN), Policy Gradient methods,Actor-Critic, etc.