comparison supervised unsupervised and reinforcement learning

 

📊 Comparison Table

AspectSupervised LearningUnsupervised LearningReinforcement Learning
DefinitionLearns from labeled data (input-output pairs).Learns from unlabeled data (only inputs).Learns by interacting with an environment and receiving feedback.
GoalPredict outcomes (e.g., classification or regression).Discover hidden patterns or structure in data.Learn a sequence of actions to maximize long-term reward.
Input DataLabeled data (e.g., image + label "cat").Unlabeled data (e.g., images only).States from environment; rewards as feedback.
OutputSpecific predictions (e.g., class label or number).Clusters, groups, or associations.Policy (strategy) or action sequence.
ExamplesSpam detection, stock price prediction, image recognition.Customer segmentation, anomaly detection, topic modeling.Game playing, robot navigation, dynamic pricing.
Training ApproachLearn mapping from inputs to known outputs.Find structure or grouping in data.Trial and error: learn from experience and rewards.
Feedback TypeDirect and correct labels for each input.No feedback, only input features.Reward signal (positive or negative).
Popular AlgorithmsLinear Regression, SVM, Decision Trees, Neural Networks.K-Means, PCA, DBSCAN, Autoencoders.Q-Learning, Deep Q-Networks (DQN), Policy Gradient Methods.

🎓 Summary :

  • Supervised Learning is best when you have labeled data and want the model to predict something specific.

  • Unsupervised Learning is used when you want the system to explore and find patterns in data with no labels.

  • Reinforcement Learning is ideal when an agent must interact with an environment, learn from rewards, and make decisions over time.

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