support vector regression #23
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The SVR test doesn't give enough data to debug |
…ved on in the future)
…setting for tests and also research implementation more
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this shouldnt have been merged |
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"
Support Vector Regression (SVR) is a type of regression algorithm based on the principles of Support Vector Machines (SVM). Unlike traditional regression methods, which aim to minimize the error for every data point, SVR focuses on fitting the best line within a certain threshold, called epsilon (ε), around the data. This threshold defines a margin within which predictions can deviate from the actual values without penalty, allowing for a more robust model that is less sensitive to outliers. SVR can use different kernel functions (such as linear, polynomial, or RBF) to handle linear and nonlinear relationships in data, making it versatile for various regression tasks. By optimizing a margin around the predicted values while minimizing errors outside of this margin, SVR balances the model's complexity with its ability to generalize to new data.
" - CHATGBT