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What is the main concept behind Support Vector Machines (SVMs)?
When are Support Vector Machines (SVMs) especially useful?
What are Support Vectors in SVMs?
What is the ‘margin’ in Support Vector Machines (SVMs)?
What does a ‘hard margin’ SVM model emphasize?
What does a ‘soft margin’ SVM model prioritize?
What is the purpose of SVM Kernels?
Which kernel type is best suited for linearly separable data?
What is the main drawback of Support Vector Machines (SVMs) in terms of computational performance?
How does regularization help prevent overfitting in Support Vector Machines (SVMs)?
What real-world application can Support Vector Machines (SVMs) be used for?
Which dataset is commonly used as an example for applying Support Vector Machines (SVMs)?
What Python library is commonly used for implementing Support Vector Machines (SVMs)?
What metric is used to evaluate the predictions of an SVM model?
What is a key advantage of Support Vector Machines (SVMs) compared to other classification methods?
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