What is the effect of using a linear kernel in SVM?

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Question: What is the effect of using a linear kernel in SVM?

Options:

Correct Answer: It can only classify linearly separable data

Solution:

Using a linear kernel in SVM means that the model can only create linear decision boundaries, making it suitable for linearly separable data.

What is the effect of using a linear kernel in SVM?

What is the effect of using a linear kernel in SVM?

Step 1: Understand what SVM (Support Vector Machine) is. It is a type of machine learning model used for classification tasks.
Step 2: Know that a kernel in SVM is a function that helps to transform data into a higher dimension to make it easier to classify.
Step 3: Learn that a linear kernel means the SVM will only create straight lines (linear decision boundaries) to separate different classes of data.
Step 4: Realize that using a linear kernel is best when the data can be separated by a straight line (linearly separable data).
Step 5: Understand that if the data is not linearly separable, a linear kernel may not perform well, as it cannot create complex boundaries.

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