What is Supervised Learning?
Supervised learning is a fundamental aspect of machine learning where algorithms are trained using labeled datasets. The process involves providing the model with input-output pairs; the input is the data you want to analyze, and the output is the known answer or label associated with that data.
In supervised learning, the goal is to learn a mapping from inputs to outputs. During training, the algorithm makes predictions based on input features and adjusts its parameters to minimize the difference between its predictions and the actual labels. This iterative process continues until the model achieves satisfactory performance.
There are two primary types of supervised learning tasks: classification and regression. Classification tasks involve predicting discrete labels, such as determining if an email is 'spam' or 'not spam.' Regression tasks, on the other hand, involve predicting continuous values, such as forecasting house prices based on various features.
Common algorithms used for supervised learning include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. Successful implementation of supervised learning has applications in numerous fields, including finance, healthcare, and marketing, making it a vital area within the broader domain of machine learning.