Supervised vs Unsupervised Learning

Supervised Learning

In supervised learning, the algorithm learns from labeled training data - each example has both input features and the correct output (label). The goal is to learn a mapping function that can predict outputs for new, unseen inputs.

Key characteristics:

• Training data includes both inputs and correct answers
• Algorithm learns to predict outputs by finding patterns between inputs and labels
• Performance can be directly measured against known correct answers

Common applications: Classification (spam detection, image recognition, decision trees) and Regression (price prediction, weather forecasting)

vs. Unsupervised Learning

Unsupervised learning works with unlabeled data - only input features, no correct answers. The algorithm must discover hidden patterns or structures in the data on its own.

Key differences:

• No labels or correct answers provided
• Algorithm finds patterns, groups, or representations independently
• Evaluation is more subjective since there’s no ground truth

Common applications: Clustering (customer segmentation), dimensionality reduction (data visualization), anomaly detection - see Types of Unsupervised Learning for details

vs. Reinforcement Learning

Reinforcement learning involves an agent learning through interaction with an environment, receiving rewards or penalties for actions taken.

Key differences:

• No direct input-output pairs; instead learns from consequences of actions
• Feedback is delayed and sparse (rewards/penalties)
• Must balance exploration (trying new things) with exploitation (using what works)

Common applications: Game playing, robotics, autonomous vehicles, recommendation systems

The choice between these approaches depends on your data availability (labeled vs. unlabeled) and problem type (prediction, pattern discovery, or sequential decision-making).