Supervised learning is a type of machine learning where the algorithm learns from labeled training data. The goal is to predict the output for new, unseen data based on the patterns learned from the training data.
Key Concepts
- Training Data: This is a dataset where each data point is labeled with the correct output.
- Model: The algorithm that learns from the training data.
- Prediction: The output generated by the model for new data.
Types of Supervised Learning
- Regression: The task is to predict a continuous value.
- Example: Predicting house prices based on features like size, location, etc.
- Classification: The task is to predict a categorical value.
- Example: Classifying emails as spam or not spam.
Common Algorithms
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees
- Random Forest
- Neural Networks
Further Reading
For more information on supervised learning, check out our Introduction to Machine Learning.
Images
Linear Regression
Neural Networks