Naive Bayes. Answer the following questions 1) What problem is Laplace smoothing

ID: 3721437 • Letter: N

Question

Naive Bayes. Answer the following questions

1) What problem is Laplace smoothing solving in Naive Bayes classifier?

a) the problem that we might see some feature values in the test data that were not seen in the training data

b) the problem that some feature values in the training data might occur much more than in the test data.

2) Generally, which is faster to make predictions?

a) a KNN classifier

b) a Naive Bayes classifier

3) (True/False) Input features that are strongly correlated will hurt the performance of a Naive Bayes classifier.

4) (Yes/No) If you increase the (relative) prior probability of a class, do you increase the chance of predictions of that class?

5) Generally, what kind of classifier do you think could more easily incorporate the addition of a new input feature?

a) Naive Bayes classifier

b) classification tree

1. a) the problem that we might see some feature values in the test data that were not seen in the training data

2. b) Naive Bayer classifier is faster to make predictions

3. True, strongly correlated input features will decrease the performance of a Naive Bayes classifier

4. Yes

5. a) Naive Bayer Classifier, it can easily incorporate new input features

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.