What isSupervised Learning
A type of machine learning where the algorithm learns from labeled data. The data includes input features and corresponding output labels.
This means that during training, the algorithm is provided with examples where the correct answer (label) is already known. The goal is for the algorithm to learn a mapping function that can accurately predict the output label for new, unseen input data.
Common Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Support Vector Machines (SVM)
- Decision Trees
- Random Forests
- Neural Networks
Example
Imagine you want to train a model to classify emails as either 'spam' or 'not spam'. You would collect a dataset of emails, and for each email, you would manually label it as either 'spam' or 'not spam'. This labeled data is then used to train a supervised learning algorithm. The algorithm learns to identify patterns and features in the emails that are indicative of spam, such as certain keywords, sender addresses, or email structure. Once trained, the model can then be used to classify new, unseen emails as either 'spam' or 'not spam'.
Supervised learning is widely used in various applications, including image recognition, natural language processing, and fraud detection.