They use kernel functions to handle non-linear data
They require a large amount of labeled data
They are not suitable for multi-class classification
Questions & Step-by-Step Solutions
Which of the following is a key feature of SVMs?
Step 1: Understand what SVM stands for - SVM means Support Vector Machine, which is a type of algorithm used in machine learning.
Step 2: Learn about kernel functions - Kernel functions are mathematical tools that help SVMs to work with data that is not linearly separable.
Step 3: Know what transforming data means - Transforming data into higher dimensions means changing the way we look at the data so that it can be separated more easily.
Step 4: Recognize the importance of handling non-linear relationships - Non-linear relationships are situations where data points cannot be separated by a straight line, and SVMs can manage these situations effectively.
