What is a potential drawback of using Support Vector Machines?
Practice Questions
Q1
What is a potential drawback of using Support Vector Machines?
They are computationally expensive for large datasets
They cannot handle multi-class classification
They require no feature scaling
They are not suitable for high-dimensional data
Questions & Step-by-Step Solutions
What is a potential drawback of using Support Vector Machines?
Step 1: Understand what Support Vector Machines (SVM) are. They are a type of machine learning algorithm used for classification tasks.
Step 2: Recognize that SVMs work by finding the best boundary (or hyperplane) that separates different classes in the data.
Step 3: Identify that when the dataset is small, SVMs can be efficient and quick to train.
Step 4: Learn that as the size of the dataset increases, the complexity of the calculations needed to find the best boundary also increases.
Step 5: Realize that this increased complexity can lead to longer training times and require more computational resources (like memory and processing power).
Step 6: Conclude that this is a potential drawback of using Support Vector Machines, especially with large datasets.
