Difference Between Supervised and Unsupervised Learning

Based on the diagram:

Supervised Learning (left side):
- Starts with labeled data (each input has a known output or category).
- The data is fed into a model, which learns the mapping from inputs to outputs.
- Goal: Predict or classify new data based on what the model has learned.
- Example: Predicting house prices when given historical prices (labels).
Unsupervised Learning (right side):
- Starts with unlabeled data (no predefined outputs).
- The algorithm groups the data into clusters based on similarities.
- Goal: Find hidden structures or patterns in the data.
- Example: Grouping customers into segments without knowing categories beforehand.

👉 In short: Supervised learning learns from labeled examples, while unsupervised learning discovers patterns in unlabeled data.

Here’s an expanded explanation with real-world examples tied to the diagram:

How it works: The model is trained with input–output pairs (e.g., features + correct labels).
Goal: Learn the relationship between inputs and outputs to make predictions.
Real-world examples:
- Email Spam Detection → Labeled emails (“spam” or “not spam”).
- House Price Prediction → Features like size, location, and age mapped to actual sale prices.
- Medical Diagnosis → Patient data labeled with known diseases.

How it works: The model has no labels and must discover structure in the data.
Goal: Group similar data points or reduce complexity.
Real-world examples:
- Customer Segmentation → Grouping shoppers by purchasing behavior without predefined categories.
- Market Basket Analysis → Finding which products are often bought together.
- Anomaly Detection → Spotting unusual credit card transactions without labeled fraud data.

👉 Key difference from the diagram:

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