Support Vector Regression (SVR) is a powerful tool for machine learning modeling. It is a supervised learning algorithm that can be used for both regression and classification tasks. SVR is a type of Support Vector Machine (SVM) that uses a linear function to map input data points to a higher dimensional space. The goal of SVR is to find the best hyperplane that separates the data points in the higher dimensional space. This hyperplane is then used to make predictions on unseen data points. SVR is a powerful tool for machine learning modeling because it can handle non-linear data, is robust to outliers, and can be used for both regression and classification tasks. Additionally, SVR is computationally efficient and can be used for large datasets.
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