-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Certification Exam Questions with AI Explanations

QUESTION: 21

Select the correct problems which can be solved using SVMs

SVMs are helpful in text and hypertext categorization
Classification of images can also be performed using SVMs
SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly
Hand-written characters can be recognized using SVM

Answer(s): A,B,C,D

Explanation:

SVMs can be used to solve various real world problems:
· SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings. · Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
· SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
· Hand-written characters can be recognized using SVM

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QUESTION: 22

Which is an example of supervised learning?

PCA
k-means clustering
SVD
EM
SVM

Answer(s): E

Explanation:

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QUESTION: 23

Which of the following are point estimation methods?

MAP
MLE
MMSE

Answer(s): A,B,C

Explanation:

Point estimators
· minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
· best linear unbiased estimator (BLUE)
· minimum mean squared error (MMSE)
· median-unbiased estimator, minimizes the risk of the absolute-error loss function · maximum likelihood (ML)
· method of moments, generalized method of moments

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QUESTION: 24

In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model.
When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because

The normalizing constant is always very close to 1
The normalizing constant only has a small impact on the maximum likelihood
The normalizing constant is often zero and can cause division by zero
The normalizing constant doesn't impact the maximizing value

Answer(s): D

Explanation:

(Change the explanation even it is correct)A normalizing constant is positive, and multiplying or dividing a series of values by a positive number does not affect which of them is the largest. Maximum likelihood estimation is concerned only with finding a maximum value, so normalizing constants can be ignored.

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QUESTION: 25

Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y. It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X

This is typically calculated by summing the joint probability distribution over Y.
This is typically calculated by integrating the joint probability distribution over Y
This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y
This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.

Answer(s): A,B,C,D

Explanation:

Given two random variables X and Y whose joint distribution is known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y. It is the probability distribution of X when the value of Y is not known. This is typically calculated by summing or integrating the joint probability distribution over Y. ' For discrete random variables, the marginal probability mass function can be written as Pr(X = x).
This is

where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out. Bivariate marginal and joint probabilities for discrete random variables are often displayed as two- way tables.
Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is

where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.
Note that a marginal probability can always be written as an expected value:

Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general