What is a common challenge when using SVM for large datasets?
Practice Questions
Q1
What is a common challenge when using SVM for large datasets?
High interpretability
Scalability and computational cost
Low accuracy
Limited feature selection
Questions & Step-by-Step Solutions
What is a common challenge when using SVM for large datasets?
Step 1: Understand that SVM stands for Support Vector Machine, which is a type of algorithm used for classification tasks.
Step 2: Recognize that SVM works by finding the best boundary (or hyperplane) that separates different classes in the data.
Step 3: Realize that when the dataset is small, SVM can quickly find this boundary without much difficulty.
Step 4: Acknowledge that as the dataset grows larger, the number of calculations needed to find the boundary increases significantly.
Step 5: Understand that this increase in calculations can make SVM slow and require a lot of memory, which is referred to as being 'computationally intensive.'
Step 6: Conclude that because of this, SVM may not work well or may take a long time to process large datasets, leading to challenges in scalability.
