Supervised learning is a type of machine learning algorithm that is based on training data that includes both the input and the corresponding expected output. The algorithm takes these pairs of input and output and learns a function that maps from one to the other. This function can then be used to predict the output for new, unseen inputs.

In the context of supervised learning, there are two main categories:

1. Classification: The output variable (label) is a category, such as “spam” or “not spam,” “fraudulent” or “valid.” Common algorithms include logistic regression, decision trees, support vector machines, and neural networks.
2. Regression: The output variable is a real or continuous value, such as weight or price. Common algorithms for regression include linear regression, polynomial regression, and various forms of neural networks.

The supervised learning process generally involves the following steps:

1. Collecting Data: Gathering a set of examples with inputs and corresponding outputs.
2. Preprocessing Data: Cleaning and transforming the data to make it suitable for training.
3. Splitting Data: Dividing the data into training and test sets to evaluate performance.
4. Selecting a Model: Choosing an appropriate algorithm or model to represent the underlying pattern.
5. Training the Model: Using the training data to adjust the model’s parameters to minimize error.
6. Evaluating the Model: Assessing the model’s performance on unseen data (usually the test set) to gauge its ability to generalize.
7. Tuning and Optimization: Adjusting hyperparameters and using techniques like cross-validation to fine-tune the model.
8. Deployment: Integrating the trained model into a production environment to make real-time predictions.