Supervised learning is a type of machine learning where the algorithm learns from labeled training data. The goal is to learn a mapping from input to output variables, so that the algorithm can predict the output for new, unseen data.
Key Concepts
- Training Data: This is the dataset used to train the model. It should be labeled, meaning each data point is associated with a correct output.
- Model: The mathematical representation of the learned mapping.
- Predictions: The output of the model when given new input data.
Types of Supervised Learning
- Regression: The goal is to predict a continuous value.
- Example: Predicting house prices based on features like size, location, etc.
- Classification: The goal is to predict a discrete value, usually represented as a class label.
- Example: Classifying emails as spam or not spam based on their content.
Example
Let's say you want to build a model to predict whether a given email is spam or not. You would start by collecting a dataset of emails, labeled as spam or not spam. You would then use this dataset to train a classification model, such as a decision tree or a neural network.
Further Reading
For more information on supervised learning, check out our Introduction to Machine Learning.