Which of the following is a disadvantage of using SVM?

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Question: Which of the following is a disadvantage of using SVM?

Options:

Correct Answer: It is sensitive to the choice of kernel

Solution:

SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.

Which of the following is a disadvantage of using SVM?

Which of the following is a disadvantage of using SVM?

Step 1: Understand what SVM (Support Vector Machine) is. It is a type of machine learning algorithm used for classification tasks.
Step 2: Learn about kernels in SVM. A kernel is a function that transforms data into a higher dimension to make it easier to classify.
Step 3: Recognize that there are different types of kernels (like linear, polynomial, and radial basis function). Each kernel can perform differently based on the data.
Step 4: Realize that choosing the wrong kernel can lead to poor performance of the SVM model.
Step 5: Understand that tuning the kernel (choosing the right one and adjusting its parameters) requires time and expertise.
Step 6: Conclude that the sensitivity to the choice of kernel is a disadvantage of using SVM.

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