Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?

The correct answer is D. You will try to reduce the features.

SVM is a supervised machine learning algorithm that can be used for classification and regression tasks. It works by finding a hyperplane in a high-dimensional space that separates the data points into two classes. The hyperplane is chosen so that it is as far as possible from the data points of both classes.

If the SVM model is underfitting, it means that the hyperplane is not able to separate the data points well. This can happen if the data set is too small or if the features are not well-chosen. In this case, you can try to reduce the number of features by removing features that are not relevant to the classification task. This can help to improve the performance of the SVM model.

The other options are not correct. Option A is not correct because increasing the number of data points will not necessarily improve the performance of the SVM model. In fact, if the data set is too large, it can actually make the problem worse. Option B is not correct because decreasing the number of data points will also not necessarily improve the performance of the SVM model. Option C is not correct because calculating more variables will not necessarily improve the performance of the SVM model. In fact, if the variables are not well-chosen, it can actually make the problem worse.