Which of the following is a disadvantage of using SVM?
Practice Questions
1 question
Q1
Which of the following is a disadvantage of using SVM?
It can handle large datasets efficiently
It is sensitive to the choice of kernel
It provides probabilistic outputs
It is easy to interpret
SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.
Questions & Step-by-step Solutions
1 item
Q
Q: Which of the following is a disadvantage of using SVM?
Solution: SVM is sensitive to the choice of kernel, which can significantly affect its performance and requires careful tuning.
Steps: 6
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.
