The correct answer is: A. We can still classify data correctly for given setting of hyper parameter C.
When the value of C is very large, the SVM model will try to classify all data points correctly, even if some of them are error prone. This is because the model will not want to sacrifice any data points, even if they are likely to be incorrect. As a result, the model may overfit the data and make poor predictions on new data.
However, if the data is very clean and there are no error prone data points, then using a very large value of C may be beneficial. This is because the model will be able to learn the true relationship between the features and the labels, and will be able to make accurate predictions on new data.
In general, it is important to choose a value of C that is appropriate for the data. If the data is error prone, then a smaller value of C should be used. If the data is clean, then a larger value of C may be beneficial.
Here is a brief explanation of each option:
- Option A: We can still classify data correctly for given setting of hyper parameter C. This is the correct answer, as explained above.
- Option B: We can not classify data correctly for given setting of hyper parameter C. This is not the correct answer, as the model will still be able to classify some data points correctly, even if the value of C is very large.
- Option C: Can’t Say. This is not the correct answer, as it is possible to classify data correctly for a given setting of hyper parameter C.
- Option D: None of these. This is not the correct answer, as Option A is the correct answer.