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 variables (X) to labels (Y) and then use this mapping to predict the label of new, unseen data.

Key Concepts

  • Training Data: This is a set of data that has been labeled with the correct output. For example, in image recognition, the training data would consist of images labeled with the correct object they contain.
  • Features: These are the input variables that the algorithm uses to make predictions. For example, in a housing price prediction model, the features might include the number of bedrooms, square footage, and location.
  • Labels: These are the output variables that the algorithm tries to predict. For example, in a binary classification problem, the label might be "yes" or "no".

Types of Supervised Learning

  • Classification: This is used when the output variable is categorical. For example, predicting whether an email is spam or not.
  • Regression: This is used when the output variable is continuous. For example, predicting the price of a house.
  • Anomaly Detection: This is used to identify outliers in data. For example, identifying fraudulent credit card transactions.

Example

Imagine you are working on a project to classify emails as either "spam" or "not spam". You would start by collecting a dataset of emails that have been labeled as "spam" or "not spam". You would then use this data to train a machine learning model to classify new emails.

Here is an example of how you might structure your training data:

| Email Content | Label |
| --- | --- |
| This is a spam email. | Spam |
| This is a legitimate email. | Not Spam |
| Another spam email. | Spam |
| Yet another legitimate email. | Not Spam |

Further Reading

To learn more about supervised learning, you can check out the following resources:

Supervised Learning