What are the limitations of SVM?

What are the limitations of Support Vector Machines?

Support Vector Machines (SVM) have several limitations, including sensitivity to outliers, high computational cost, and difficulty in handling large datasets.

Can you explain why SVM is sensitive to outliers?

SVM aims to maximize the margin between classes, and outliers can significantly affect the position of the decision boundary, leading to poor generalization.

What about the computational cost? Why is it high?

The computational cost of SVM is high because it involves solving a quadratic optimization problem, which can be time-consuming, especially with large datasets.

Are there any scenarios where SVM performs well despite its limitations?

Yes, SVM performs well in high-dimensional spaces and is effective in cases where the number of dimensions exceeds the number of samples.