How can you interpret the contribution of a specific feature to the Naive Bayes decision?

By examining the ratio of its conditional probabilities across classes

By counting the number of times the feature appears in the training data

By checking if the feature is the first in the dataset

By looking at the feature's variance across all instances

A Naive Bayes model is used to detect rare diseases. The dataset has 99% negative cases and 1% positive cases. The model achieves 99% accuracy on the test set. However, the recall for the positive class is only 20%. Which conclusion is most appropriate?

The model is not useful because it misses 80% of actual positive cases, despite high accuracy.

The model has high precision, so it is acceptable.

The model is excellent because 99% accuracy is very high.

The model is overfitting because accuracy is higher than recall.

Dell Data Science Foundations

Domain 4Objective 5

Nave Bayesian Classifiers DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 1)

Part of the Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods domain, which accounts for 40% of the DATA-SCIENCE-FOUNDATIONS exam.

25questions here

5free pages

9concepts

40%of the exam

Questions 1–5

1application · easy
A data scientist is training a Naive Bayes classifier to predict whether a customer will churn (yes/no) based on two features: 'uses mobile app' (yes/no) and 'has a support ticket' (yes/no). From 100 training records, 30 are churn=yes. Among churn=yes, 20 use the app and 15 have a ticket. Among churn=no, 40 use the app and 10 have a ticket. What is the prior probability of churn=yes?
Select an answer first
2application · medium
A team has built a Naive Bayes classifier to flag fraudulent transactions. The confusion matrix shows: 90 true negatives, 5 false positives, 2 false negatives, and 3 true positives. What is the precision of the model?
Select an answer first
3application · medium
In a medical test, the probability of having a disease is 0.01. The test is 95% sensitive (true positive rate) and 90% specific (true negative rate). A patient tests positive. What is the probability that the patient actually has the disease?
Select an answer first
4foundation · easy
How can you interpret the contribution of a specific feature to the Naive Bayes decision?
Select an answer first
5expert · medium
A Naive Bayes model is used to detect rare diseases. The dataset has 99% negative cases and 1% positive cases. The model achieves 99% accuracy on the test set. However, the recall for the positive class is only 20%. Which conclusion is most appropriate?
Select an answer first

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Nave Bayesian Classifiers DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 1)

Questions 1–5

A team has built a Naive Bayes classifier to flag fraudulent transactions. The confusion matrix shows: 90 true negatives, 5 false positives, 2 false negatives, and 3 true positives. What is the precision of the model?

In a medical test, the probability of having a disease is 0.01. The test is 95% sensitive (true positive rate) and 90% specific (true negative rate). A patient tests positive. What is the probability that the patient actually has the disease?

How can you interpret the contribution of a specific feature to the Naive Bayes decision?

A Naive Bayes model is used to detect rare diseases. The dataset has 99% negative cases and 1% positive cases. The model achieves 99% accuracy on the test set. However, the recall for the positive class is only 20%. Which conclusion is most appropriate?

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