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Dell EMC Data Science and BigData Certification Questions and Answers



Question : Select the statement which applies correctlty to the Naive Bayes

: Select the statement which applies correctlty to the Naive Bayes
1. Works with a small amount of data
2. Sensitive to how the input data is prepared
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4. All of above



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Naive Bayes
Pros: Works with a small amount of data, handles multiple classes
Cons: Sensitive to how the input data is prepared
Works with: Nominal values





Question :

Select the correct statement which applies to Bayes rule

:
1. Bayesian probability and Bayes' rule gives us a way to estimate unknown probabilities from known values.
2. You can reduce the need for a lot of data by assuming conditional independence among the features in your data.
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4. Only 1 and 2
5. All 1,2 and 3 are correct


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Using probabilities can sometimes be more effective than using hard rules for classification. Bayesian probability and Bayes' rule gives us a way to estimate unknown probabilities from known values.
You can reduce the need for a lot of data by assuming conditional independence among the features in your data. The assumption we make is that the probability of one word doesn't depend on any other words in the
document. We know this assumption is a little simple. That's why it's known as naive Bayes. Despite its incorrect assumptions, naive Bayes is effective at classification.
Bayes' theorem finds the actual probability of an event from the results of your tests. For example, you can:
" Correct for measurement errors. If you know the real probabilities and the chance of a false positive and false negative, you can correct for measurement errors.
" Relate the actual probability to the measured test probability. Bayes' theorem lets you relate Pr(A|X), the chance that an event A happened given the indicator X, and Pr(X|A), the chance the indicator X happened
given that event A occurred. Given mammogram test results and known error rates, you can predict the actual chance of having cancer.






Question : Which of the following technique can be used to the design of recommender systems?
: Which of the following technique can be used to the design of recommender systems?
1. Naive Bayes classifier
2. Power iteration
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4. 1 and 3
5. 2 and 3

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Explanation: : One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on
users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine
analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user
similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation


Related Questions

Question : Refer to the exhibit
You ran a linear regression, and the final output is seen in the exhibit.
Based only on the information in the exhibit and an acceptable confidence level of 95%, how
would you interpret the interaction of variable D with the dependent variable?
 : Refer to the exhibit
1. In this model, Variable D is not significantly interacting with the dependent variable
2. For every 1 unit increase in variable D, holding all other variables constant, we can expect the
dependent variable to increase by 10.23 units
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dependent variable to be multiplied by 10.23 units
4. Variable D is more significant than variables A, B, and C.




Question : Refer to the exhibit.
The graph represents an ROC space with four classifiers labelled A through D. Which point in the
graph represents a perfect classification?
 : Refer to the exhibit.
1. Q
2. P
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4. R




Question : Refer to the exhibit
Consider the training data set shown in the exhibit. What are the classification (Y = 0 or 1) and the
probability of the classification for the tuple
X(1, 0, 0)
using Naive Bayesian classifier?

 : Refer to the exhibit
1. Classification Y = 1, Probability = 4/54
2. Classification Y = 0, Probability = 4/54
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4. Classification Y = 1, Probability = 1/54




Question : Refer to the exhibit.
You have scored your Naive bayesian classifier model on a hold out test data for cross validation
and determined the way the samples scored and tabluated them as shown in the exhibit.
What are the Precision and Recall rate of the model?

 : Refer to the exhibit.
1. Precision = 262/277
Recall = 262/288
2. Precision =262/288
Recall = 262/277
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Recall = 288/262
4. Precision = 288/262
Recall = 277/262




Question : Which ROC curve represents a perfect model fit?
 : Which ROC curve represents a perfect model fit?
1. A
2. B
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4. D




Question : Refer to the exhibit.
You have scored your Naive bayesian classifier model on a hold out test data for cross validation
and determined the way the samples scored and tabulated them as shown in the exhibit.
What are the the False Positive Rate (FPR) and the False Negative Rate (FNR) of the model?
 : Refer to the exhibit.
1. FPR = 15/262
FNR = 26/288
2. FPR = 26/288
FNR = 15/262
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FNR = 288/26
4. FPR = 288/26
FNR = 262/15