Data Science Interview
Questions 2026 | Answers,
Rationales, Study Guide &
Interview Preparation
, Updated Questions and Answers
100% Verified Exam Prep and Comprehensive
Rationales
Included
, Kernel trick Using the kernel trick to find separating hyperplanes in higher dimensional space
To solve a nonlinear problem using an SVM, we transform the training data onto a
higher dimensional feature space via a mapping function Using the kernel trick to
find separating hyperplanes in higher dimensional space and train a linear SVM
model to classify the data in this new feature space. Then we can use the same
mapping function Using the kernel trick to find separating hyperplanes in higher
dimensional space to transform new, unseen data to classify it using the linear SVM
model.
However, one problem with this mapping approach is that the construction of the
new features is computationally very expensive, especially if we are dealing with
high-dimensional data. This is where the so-called kernel trick comes into play.
Although we didn't go into much detail about how to solve the quadratic
programming task to train an SVM, in practice all we need is to replace the dot
product Using the kernel trick to find separating hyperplanes in higher dimensional
space by Using the kernel trick to find separating hyperplanes in higher dimensional
space. In order to save the expensive step of calculating this dot product between
two points explicitly, we define a so-called kernel function: Using the kernel trick to
find separating hyperplanes in higher dimensional space.
One of the most widely used kernels is the Radial Basis Function kernel (RBF
kernel) or Gaussian kernel:
The trick is to choose a transformation so that the kernel can be computed without
actually computing the transformation.
replacing the dot-product function with a new function that returns what the dot
product would have been if the data had first been transformed to a higher
dimensional space. Usually done using the radial-basis function
Radial Basis Function kernel (RBF kernel) Used for Kernel Trick in SVMs
Gaussian Kernel Used for Kernel Trick in SVMs
Types of Kernels for Kernel Trick Fisher kernel
Graph kernels
Kernel smoother
Polynomial kernel
RBF kernel
String kernels
What are kernels? A kernel is a similarity function. It is a function that you, as the domain expert,
provide to a machine learning algorithm. It takes two inputs and spits out how
similar they are.
Kernels offer an alternative. Instead of defining a slew of features, you define a
single kernel function to compute similarity between images. You provide this
kernel, together with the images and labels to the learning algorithm, and out comes
a classifier.
Questions 2026 | Answers,
Rationales, Study Guide &
Interview Preparation
, Updated Questions and Answers
100% Verified Exam Prep and Comprehensive
Rationales
Included
, Kernel trick Using the kernel trick to find separating hyperplanes in higher dimensional space
To solve a nonlinear problem using an SVM, we transform the training data onto a
higher dimensional feature space via a mapping function Using the kernel trick to
find separating hyperplanes in higher dimensional space and train a linear SVM
model to classify the data in this new feature space. Then we can use the same
mapping function Using the kernel trick to find separating hyperplanes in higher
dimensional space to transform new, unseen data to classify it using the linear SVM
model.
However, one problem with this mapping approach is that the construction of the
new features is computationally very expensive, especially if we are dealing with
high-dimensional data. This is where the so-called kernel trick comes into play.
Although we didn't go into much detail about how to solve the quadratic
programming task to train an SVM, in practice all we need is to replace the dot
product Using the kernel trick to find separating hyperplanes in higher dimensional
space by Using the kernel trick to find separating hyperplanes in higher dimensional
space. In order to save the expensive step of calculating this dot product between
two points explicitly, we define a so-called kernel function: Using the kernel trick to
find separating hyperplanes in higher dimensional space.
One of the most widely used kernels is the Radial Basis Function kernel (RBF
kernel) or Gaussian kernel:
The trick is to choose a transformation so that the kernel can be computed without
actually computing the transformation.
replacing the dot-product function with a new function that returns what the dot
product would have been if the data had first been transformed to a higher
dimensional space. Usually done using the radial-basis function
Radial Basis Function kernel (RBF kernel) Used for Kernel Trick in SVMs
Gaussian Kernel Used for Kernel Trick in SVMs
Types of Kernels for Kernel Trick Fisher kernel
Graph kernels
Kernel smoother
Polynomial kernel
RBF kernel
String kernels
What are kernels? A kernel is a similarity function. It is a function that you, as the domain expert,
provide to a machine learning algorithm. It takes two inputs and spits out how
similar they are.
Kernels offer an alternative. Instead of defining a slew of features, you define a
single kernel function to compute similarity between images. You provide this
kernel, together with the images and labels to the learning algorithm, and out comes
a classifier.
