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.