Regression predicts continuous values based on input from "summary" of Machine Learning by Ethem Alpaydin
Regression is a method used in machine learning to predict continuous values based on input data. Unlike classification, which predicts discrete classes or categories, regression aims to estimate a continuous output variable. This is achieved by fitting a mathematical model to the data, allowing us to make predictions on new, unseen instances.
In regression, the goal is to find a relationship between the input variables and the continuous output variable. This relationship is typically represented by a mathematical function that maps inputs to outputs. By analyzing the data and learning this mapping function, we can predict the value of the output variable for new input instances.
There are various regression algorithms that can be used to build predictive models, such as linear regression, polynomial regression, and support vector regression. Each algorithm has its own strengths and weaknesses, and the choice of algorithm depends on the nature of the data and the problem at hand.
One common metric used to evaluate the performance of a regression model is the mean squared error (MSE), which measures the average squared difference between the predicted values and the actual values. A lower MSE indicates a better fit of the model to the data.
Regression is widely used in various fields, including economics, finance, and healthcare. It is a powerful tool for making predictions and understanding relationships between variables in complex systems. By leveraging regression analysis, we can uncover hidden patterns in the data and make informed decisions based on these insights.
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