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SVR stands for Support Vector Regression, which is a machine learning algorithm used for regression tasks. It is based on the concept of support vector machines (SVM) and aims to predict continuous output values. In SVR, the algorithm finds a hyperplane that maximizes the margin between the predicted values and a certain deviation called epsilon tube. This allows it to deal with data points that fall within this tube without being penalized.To determine the hyperplane, SVR uses support vectors, which are data points that lie on the edge of the tube. These support vectors are crucial for making predictions as they define the hyperplane.Compared to other regression algorithms, SVR can handle non-linear relationships between features and target variables by using different kernel functions. Some popular kernels include linear, polynomial, and radial basis function.Overall, SVR is a powerful tool for regression tasks that can handle complex data patterns and provide accurate predictions when properly implemented.

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