What is the effect of using a linear kernel in SVM?
Practice Questions
1 question
Q1
What is the effect of using a linear kernel in SVM?
It allows for non-linear decision boundaries
It simplifies the model and reduces computation
It increases the risk of overfitting
It can only classify linearly separable data
Using a linear kernel in SVM means that the model can only create linear decision boundaries, making it suitable for linearly separable data.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the effect of using a linear kernel in SVM?
Solution: Using a linear kernel in SVM means that the model can only create linear decision boundaries, making it suitable for linearly separable data.
Steps: 5
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.
