The correct answer is: TRUE.
Support vector regression (SVR) is a supervised machine learning algorithm used for regression. It is a type of support vector machine (SVM) that finds a hyperplane in a high-dimensional space that separates the data points into two classes. The hyperplane is chosen such that it minimizes the distance between the data points and the hyperplane.
In SVR, we try to fit the error within a certain threshold. This means that we want to find a hyperplane that separates the data points into two classes such that the distance between the data points and the hyperplane is less than or equal to a certain threshold. This threshold is called the margin.
The margin is a measure of how well the hyperplane separates the data points. A larger margin indicates that the hyperplane is a better fit for the data.
SVR is a powerful machine learning algorithm that can be used to solve a variety of regression problems. It is particularly well-suited for problems where the data is noisy or where there are outliers.
Here is a brief explanation of each option:
- A. TRUE. In SVR we try to fit the error within a certain threshold. This means that we want to find a hyperplane that separates the data points into two classes such that the distance between the data points and the hyperplane is less than or equal to a certain threshold. This threshold is called the margin.
- B. FALSE. In SVR we do not try to fit the error exactly. We try to fit the error within a certain threshold. This means that we are willing to tolerate some error, as long as the error is within the specified threshold.