Supervised learning is a type of machine learning where the algorithm learns from labeled training data. The goal of supervised learning is to predict the output for new, unseen data based on the patterns it has learned from the training data.
Common Types of Supervised Learning Algorithms
- Linear Regression: Used for predicting continuous values.
- Logistic Regression: Used for predicting binary outcomes.
- Support Vector Machines (SVM): Used for both classification and regression.
- Decision Trees: Used for both classification and regression.
- Random Forests: An ensemble method that combines multiple decision trees.
Real-World Applications
Supervised learning is used in various fields, including:
- Medical Diagnosis: Predicting diseases based on patient data.
- Financial Fraud Detection: Identifying fraudulent transactions.
- Image Recognition: Identifying objects in images.
Further Reading
For more information on supervised learning, you can visit our Machine Learning Basics page.