What is the main purpose of using a kernel in SVM?

Transform data into higher-dimensional space

Reduce computational complexity

Simplify the hyperplane equation

Which type of SVM kernel is most useful for handling non-linear separation problems?

Radial Basis Function (RBF) kernel

Which of the following statements is true regarding SVM kernel functions?

Different kernel functions can be specified in SVM, including custom kernels.

There is only one fixed kernel function in SVM

SVM cannot use custom kernel functions.

SVM always uses a linear kernel.

In Bayesian networks, what do we estimate when dealing with discrete nodes?

Variance and standard deviation

What does the acronym "MAP" stand for in ML estimation?

Maximum Approximation Probability

In ML estimation, what is the purpose of taking the logarithm of the likelihood function?

To change the product of probabilities into a sum of logarithms

To simplify the likelihood function

To convert it into a polynomial

To ensure convergence of the estimator

What is the primary advantage of using hierarchical clustering over other methods in cases where the number of clusters is unknown?

Hierarchical clustering does not require specifying the number of clusters in advance.

Hierarchical clustering is faster.

Hierarchical clustering is less sensitive to noise in the data.

How does a search engine benefit from clustering?

It improves search result quality.

It reduces the number of indexed web pages.

. It increases the speed of web crawling.

It enhances the user interface.

What is Bayes' theorem used for in Naïve Bayes?

To calculate the posterior probability

To calculate the likelihood probability

To determine the prior probability

To estimate the marginal probability

In Naïve Bayes, what does the "Naïve" part of the name refer to?

The assumption of feature independence

The use of Bayesian probability

The simplicity of the algorithm

What type of data does the Gaussian Naïve Bayes Classifier assume for its features?

Continuous and normally distributed

Binary and Bernoulli-distributed

In clustering, what is the primary goal of creating clusters?

To maximize similarity within clusters and minimize similarity between clusters.

To create clusters of equal size.

To create as many clusters as possible.

To minimize the number of clusters.

What is the primary advantage of using hierarchical clustering over other methods in cases where the number of clusters is unknown?

Hierarchical clustering does not require specifying the number of clusters in advance.

Hierarchical clustering is faster.

Hierarchical clustering is less sensitive to noise in the data.

ml quiz 4

Authored by THENNARASU. S

Special Education

2nd Grade

Used 1+ times

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33 questions

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MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary purpose of Quadratic Discriminant Analysis (QDA)?

To find the separating hyperplane in SVM

To classify data points into two classes

To generate non-linear decision boundaries

To perform dimensionality reduction

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In LDA, what happens when the mean of distributions is shared between classes?

LDA can create a new axis for separation.

LDA becomes computationally efficient.

LDA fails to create a linearly separable axis.

LDA produces a higher-dimensional output.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the primary difference between Linear Discriminant Analysis (LDA) and Flexible Discriminant Analysis (FDA)?

LDA is used for binary classification, while FDA is for multi-class problems.

LDA assumes linear groups of inputs, while FDA handles non-linear groups.

LDA applies regularization, while FDA does not.

LDA works only for image processing, while FDA is for finance.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

ML estimation aims to maximize which function?

Loss function

Probability function

Likelihood function

Error function

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What step follows the differentiation of the log-likelihood function with respect to θ in ML estimation?

Constructing the likelihood function

Taking the logarithm of the likelihood

Setting the derivative to zero

Using properties of i.i.d samples

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

In SVM, what is the primary goal of finding a hyperplane?

Minimize the margin between classes

Maximize the margin between classes

Reduce the number of support vectors

Create a non-linear decision boundary

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the data points closest to the hyperplane called in SVM?

Decision vectors

Feature vectors

Support vectors

Kernel vectors

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ml quiz 4

What is the primary purpose of Quadratic Discriminant Analysis (QDA)?

In LDA, what happens when the mean of distributions is shared between classes?

What is the primary difference between Linear Discriminant Analysis (LDA) and Flexible Discriminant Analysis (FDA)?

ML estimation aims to maximize which function?

What step follows the differentiation of the log-likelihood function with respect to θ in ML estimation?

In SVM, what is the primary goal of finding a hyperplane?

What are the data points closest to the hyperplane called in SVM?

Which type of SVM is suitable for linearly separable data?

What is the main purpose of using a kernel in SVM?

Which type of SVM kernel is most useful for handling non-linear separation problems?

Access all questions and much more by creating a free account

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