Question 1

Word Embedding describes a task in natural language processing (NLP) where:

Options :

A : Words are converted into numerical vectors.

B : Words are featurized by taking a histogram of letter counts.

C : Words are featurized by taking a matrix of bigram counts.

D : Words are grouped together into clusters and then represented by word cluster membership.

Answer: A

Question 2

A classifier has been implemented to predict whether or not someone has a specific type of disease. Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

Options :

A : Mean squared error

B : Precision and accuracy

C : Precision and recall

D : Recall and explained variance

Answer: C

Question 3

Which of the following is NOT a valid cross-validation method?

Options :

A : Bootstrapping

B : K-fold

C : Leave-one-out

D : Stratification

Answer: D

Question 4

Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression. What should you do before log-transforming Y?

Options :

A : Add 1 to all of the Y values.

B : Divide all the Y values by the standard deviation of Y.

C : Explore the data for outliers.

D : Subtract the mean of Y from all the Y values.

Answer: A

Question 5

Which of the following equations best represent an LI norm?

Options :

A : |x| + |y|

B : |x|+|y|^2

C : |x|-|y|

D : |x|^2+|y|^2

Answer: A

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