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NEW QUESTION: 1
Refer to the Exhibit.
On the EMC Data Domain system, what is the approximate amount of data sent to the system on November
11th?
A. 220 GiB
B. 70 GiB
C. 200 GiB
D. 80 GiB
Answer: A
NEW QUESTION: 2
Which year did the same number of boys and girls attend the conference?
A. 0
B. None
C. 1
D. 2
E. 3
Answer: E
NEW QUESTION: 3
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering