13. Which is not true statement about Kernel Trick*
2 point
-A Kernel Trick is a method where a Non Linear data is projected onto a higher
dimension space so as to make it easier to classify the data where it could be linearly
divided by a plane.
-A Kernel Trick is a method of transforming the original (non-linear) input data into a
higher dimensional space (as a linear representation of data).
-The Kernel Trick allows us to take linear Support Vector Machines and extend their
functionality to classify non-linear data sets.
-A Kernel Trick is a method which can easily separates the data points in a lower
dimensionality space
Answers
Answer:
Explanation:
A Kernel Trick is a basic way for projecting non-linear data onto a higher-dimensional space in order to make it easier to classify the data in areas where it may be divided linearly by a plane. This is accomplished mathematically through the use of Lagrangian multipliers and the Lagrangian formula.
The kernel's job is to convert data into the appropriate format. Different SVM algorithms employ different types of kernel functions.
Functions are available in a variety of sizes and shapes. Linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid are among examples.
Therefore, Option (c) A Kernel Trick is a method of transforming the original (non-linear) input data into a higher dimensional space (as a linear representation of data) is not the true statement.
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Answer:
The correct answer of this question is option c.
Explanation:
Given - Kernel Trick.
To Find - Which is not true statement about Kernel Trick.
A Kernel Trick is a simple method for projecting non-linear data onto a higher-dimensional space so that it may be classified more easily in places where it can be split linearly by a plane. The Lagrangian multipliers and the Lagrangian formula are used to do this mathematically.
The incorrect option is option c which is a Kernel Trick is a method of transforming the original (non-linear) input data into a higher dimensional space (as a linear representation of data).
The kernel's job is to transform data into a format that can be used. Different types of kernel functions are used by different SVM algorithms. Functions come in a wide range of sizes and forms. Examples include linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.
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