Association and Apriori Execution Test - Machine Learning

Question 1

ASSOCIATION analysis is a valuable tool to interpret causal relationship of events

Answer

true

Question 2

Antecedents & Consequents should be marked at the beginning of any ASSOCIATION analysis

Answer

true

Question 3

Main pillars of association analysis are Frequency of itemsets and Reliability of rules found

Answer

true

Question 4

Antecedent support is its relative frequency, also called Prior Confidence

Answer

true

Question 5

“Lift” measures the ratio of rule’s confidence to the overall support of antecedents

Answer

true

Question 6

Using confidence ratio as rule selection criterion can help to find interesting rules with low prior confidence

Answer

true

Question 7

Using confidence ratio as rule selection can help us to find lots of trivial rules for very common consequents

Answer

true

Question 8

Rule support is always lower or equal than Support

Answer

true

Question 9

For a market basket analysis, tabular data normally requires specifying a customer field and dummy product variables

Answer

true

Question 10

APRIORI algorithm allows multiple consequents

Answer

true

Question 11

Transactional data is the preferred format for customer-level predictions

Answer

true

Question 12

Approximately 5.6% of tickets contain yogurt and whole milk

Answer

true

Question 13

40.31% of tickets containing whole milk include also tropical fruit

Answer

true

Question 14

1,372 tickets verify rule #20

Answer

true

Question 15

Buyers of root_vegetables have bought approx. 2.25 more other vegetables than the average buyers of other vegetables

Answer

true

Question 16

Less than 25% of customers bought “other vegetables”

Answer

true

Question 17

10.9% of customers bought “other vegetables”

Answer

true

Question 18

If we set Confidence Difference lower bound to 60, we would not get rules from root_vegetables

Answer

true

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