What is/are true about kernel in SVM? 1. Kernel function map low dimensional data to high dimensional space 2. It’s a similarity function

1
2
1 and 2
None of these

The correct answer is C. 1 and 2.

A kernel function is a function that maps data points from a low-dimensional space to a high-dimensional space. This is done in order to find a more efficient way to classify data points. The kernel function is a similarity function, which means that it measures the similarity between two data points.

Here is a more detailed explanation of each option:

  • Option 1: Kernel function map low dimensional data to high dimensional space.

This is true because the kernel function maps data points from a low-dimensional space to a high-dimensional space. This is done in order to find a more efficient way to classify data points.

  • Option 2: It’s a similarity function.

This is also true because the kernel function is a similarity function, which means that it measures the similarity between two data points.

  • Option 3: None of these.

This is not true because both options 1 and 2 are true.

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