A(n) __________ matrix displays a model's correct and incorrect classification.

Ans. #1

b
RATIONALE: A classification confusion matrix displays a model's correct and incorrect classification.

Ans. #2

c
RATIONALE: Euclidean distance is a geometric measure of dissimilarity between observations based on Pythagorean Theorem.

Ans. #3

a
RATIONALE: Misclassifying an actual Class 1observation as a Class 0 observation is known as a false positive.

Ans. #4

a
RATIONALE: The conversion to z-scores makes it easier to identify outlier measurements, which can distort the Euclidean distance between observations. Standardizing (or normalizing) observations removes bias due to the difference in measurement units, and variable weighting allows the analyst to introduce appropriate bias based on the business context.

Ans. #5

b
RATIONALE: Specificity is one minus the Class 0 error rate.

Ans. #6

a
RATIONALE: When clustering observations sole on the basis of categorical variables encoded as 0-1 (or dummy variables), a better measure of similarity between two observations can be achieved by counting the number of variables with matching values. The simplest overlap measure is called the matching coefficient. To avoid misstating similarity due to the absence of a feature, a similarity measure called Jaccard's coefficient does not count matching zero entries.

All correct!

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