What are the advantages of using Support Vector Machines (SVM)?

What are the advantages of using Support Vector Machines (SVM)?

Support Vector Machines (SVM) are powerful for classification tasks. They work well in high-dimensional spaces and are effective in cases where the number of dimensions exceeds the number of samples.

Can you explain why they perform well in high-dimensional spaces?

SVMs use a technique called the kernel trick, which allows them to create hyperplanes in high-dimensional spaces without explicitly mapping the data. This helps in finding the optimal boundary between classes.

Are there any specific scenarios where SVMs are particularly useful?

Yes, SVMs are particularly useful in text classification, image recognition, and bioinformatics, where the data is often high-dimensional and complex.

What are the limitations of SVMs?

Some limitations include their sensitivity to noise, the need for careful parameter tuning, and longer training times with large datasets.