Calculating within that space becomes more and more expensive when there are more dimensions. The trick comes in at this time. It allows us to operate in the original feature space without having to calculate the coordinates of the data in a higherdimensional space.

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## What is the kernel trick used for?

The trick allows the inner product of the function. The trick is to identify the functions that can be represented in place of the mapping functions.

## When would you use a polynomial kernel?

The similarity of training samples in a feature space over the coefficients of the original variables allows learning of non- linear models.

## What is meant by kernel trick?

The trick is that kernel methods only represent the data through a set of pairwise similarity comparisons between the original data observations x and the original coordinates in the lowerdimensional space, instead of explicitly applying the transformations (x) and representing the data by these transformed…

## Who invented kernel trick?

The idea of applying the kernels trick to maximum-margin hyperplanes was first proposed by Aizerman et al. in 1992.

## What is kernel in SVM?

A function called a kernels is used to help solve problems. They give ways to avoid complicated calculations. The amazing thing about kernels is that we can go to higher dimensions and perform smooth calculations with the help of it. We can go to an infinite number of dimensions.