Supervised learning is a machine learning paradigm where the model learns from labeled training data. This means that each training example has a corresponding input-output pair, allowing the algorithm to predict outcomes for new data.
🔍 Key Concepts
- Labeled Data: Input data paired with correct outputs (e.g.,
[[X, y]]format) - Training Process: The model iteratively adjusts parameters to minimize prediction errors
- Evaluation Metrics: Accuracy, precision, recall, and F1-score are commonly used
📈 Common Applications
- Classification: Spam detection, image recognition
- Regression: Stock price prediction, temperature forecasting
- Anomaly Detection: Fraud identification in financial transactions
🧠 Popular Algorithms
| Algorithm | Use Case | Complexity |
|---|---|---|
| Linear Regression | Predicting numerical values | Low |
| Decision Trees | Rule-based classification | Medium |
| Neural Networks | Complex pattern recognition | High |
For deeper exploration, check our Machine Learning Fundamentals Guide.