Training Data
Supervised learning requires labelled training data, where each example in the dataset is paired with its corresponding output label.
Classification vs. Regression
Supervised learning tasks can be categorized into two main types:
- Classification: Predicting a categorical label or class for each input example.
- Regression: Predicting a continuous value or quantity for each input example.
Algorithms
Supervised learning algorithms include:
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Support vector machines (SVM)
- k-Nearest Neighbors (k-NN)
- Neural networks
Evaluation Metrics
Common evaluation metrics for supervised learning include accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).