Q1. You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
A.K-means
B. K-nearest neighbors
C. Lasso regression
D. Logistic regression
E. Ridge regression
Correct Answer: C, E
Q2. Which of the following tests should be performed at the production level before deploying a newly retrained model?
A.A/Btest
B. Performance test
C. Security test
D. Unit test
Correct Answer: B
Q3. You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
A.K-means
B. K-nearest neighbors
C. Lasso regression
D. Logistic regression
E. Ridge regression
Correct Answer: C, E
Q4. You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
A.When it is necessary to save computational time.
B. When the categories of the dependent variable are not linearly separable.
C. When the distribution of the dependent variable is Gaussian.
D. When there is high correlation among the features.
Correct Answer: B
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