What is Support Vector Machine Algorithm | Data Science | Machine Learning Interview Question

What is the intuition behind Support Vector Machines (SVM)?

How does the SVM algorithm classify data points into different classes?

What are the key components of an SVM model?

How do you choose the appropriate kernel function for an SVM?
What is the role of the regularization parameter (C) in SVM?
Can SVM handle both linearly separable and non-linearly separable data?
How does SVM handle multi-class classification problems?
What are the advantages of using SVM over other classification algorithms?
How does SVM perform in high-dimensional feature spaces?
What strategies can be employed to handle imbalanced datasets in SVM?
What are support vectors and how are they important in SVM?
How does SVM handle outliers in the data?
Can SVM be used for regression tasks?
How does the margin affect the performance of an SVM model?
What are some real-world applications of SVM?
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