Supervised learning is a type of machine learning that involves training an algorithm on labeled data. In this article, we will explore the basics of supervised learning, including two common types: regression and classification.
Regression
Regression is a type of supervised learning used for predicting continuous variables. In regression, the algorithm is trained on labeled data with a known outcome. The algorithm then uses this data to identify a mathematical relationship between the input variables and the output variable. This relationship can be used to make predictions on new data.
For example, if we wanted to predict the price of a house based on its size, we could use a regression algorithm trained on data that includes both the size and price of houses. The algorithm would identify the relationship between the size of a house and its price, and we could use this relationship to predict the price of a new house based on its size.
Classification
Classification is a type of supervised learning used for predicting categorical variables. In classification, the algorithm is trained on labeled data with a known outcome. The algorithm then uses this data to identify patterns in the input variables that are associated with the output variable. This pattern can be used to make predictions on new data.
For example, if we wanted to predict whether an email was spam or not, we could use a classification algorithm trained on data that includes both the content of emails and whether they were identified as spam or not. The algorithm would identify patterns in the content of emails that are associated with spam, and we could use this pattern to predict whether a new email is spam or not.
Supervised learning is a powerful technique for making predictions based on labeled data. Regression and classification are two common types of supervised learning used for predicting continuous and categorical variables, respectively. By understanding the basics of supervised learning, we can begin to explore the wide range of applications where it can be used to make accurate predictions and improve decision-making.