Overview
Supervised learning is the most common subfield of machine learning. The algorithm learns a function that maps an input to an output based on example input-output pairs.
Key Concepts
- Labeled Data: Data that has been tagged with the correct answer.
- Classification: Predicting a discrete label (e.g., spam or not spam).
- Regression: Predicting a continuous value (e.g., house prices).
Process
- Data Collection: Gathering labeled examples.
- Training: The model adjusts its parameters to minimize the difference between its predictions and the actual labels.
- Evaluation: Testing the model on unseen data to check its accuracy.