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2. One feature set is the number of detected earthquakes in a city and the other is the population in that city.

A. Scale
B. Don't scale

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For each of the following situations state whether it would be a good idea to scale the features.1. One feature set is height (in meters) and the other is weight (in kilograms).

A. Scale
B. Don't scale

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If we cut our clusters off at the dotted line above, how many elements (words) are in the largest cluster and the smallest cluster?

A. largest: 3, smallest: 1
B. largest: 4, smallest: 1
C. largest: 4, smallest: 3
D. largest: 4, smallest: 4

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If we cut our clusters off at the dotted line above, how many clusters will we have?

A. 1
B. 2
C. 3
D. 4

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5. Looking at the options they’re given, the board members choose to go with a supervised model with lead role as data. You become outraged. "How can you not include movie length? It’s incredibly important! Who watches a 3 hour long movie --" Your fellow data scientist interrupts you. "Yeah, I agree, but look at these initial results. You see, if we remove movie length, ..." What can your colleague (correctly) say to convince you? Check all that apply.

A. "we can reduce inter-cluster dissimilarity."
B. "we can reduce intra-cluster dissimilarity."
C. "the model starts to work. It doesn’t work otherwise."
D. "we can consume less memory, and the results look almost the same."

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2. One feature set is the number of detected earthquakes in a city and the other is the population in that city.

A. Scale B. Don't scale

For each of the following situations state whether it would be a good idea to scale the features.1. One feature set is height (in meters) and the other is weight (in kilograms).

If we cut our clusters off at the dotted line above, how many elements (words) are in the largest cluster and the smallest cluster?

If we cut our clusters off at the dotted line above, how many clusters will we have?

A. Scale
B. Don't scale