Association and Apriori Execution Test - Machine Learning

1. 1ASSOCIATION analysis is a valuable tool to interpret causal relationship of events

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2. 2Antecedents & Consequents should be marked at the beginning of any ASSOCIATION analysis

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3. 3Main pillars of association analysis are Frequency of itemsets and Reliability of rules found

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4. 4Antecedent support is its relative frequency, also called Prior Confidence

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5. 5“Lift” measures the ratio of rule’s confidence to the overall support of antecedents

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6. 6Using confidence ratio as rule selection criterion can help to find interesting rules with low prior confidence

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7. 7Using confidence ratio as rule selection can help us to find lots of trivial rules for very common consequents

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8. 8Rule support is always lower or equal than Support

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9. 9For a market basket analysis, tabular data normally requires specifying a customer field and dummy product variables

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10. 10APRIORI algorithm allows multiple consequents

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11. 11Transactional data is the preferred format for customer-level predictions

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12. 12Approximately 5.6% of tickets contain yogurt and whole milk

    

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13. 1340.31% of tickets containing whole milk include also tropical fruit

    

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14. 141,372 tickets verify rule #20

    

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15. 15Buyers of root_vegetables have bought approx. 2.25 more other vegetables than the average buyers of other vegetables

    

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16. 16Less than 25% of customers bought “other vegetables”

    

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17. 1710.9% of customers bought “other vegetables”

    

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18. 18If we set Confidence Difference lower bound to 60, we would not get rules from root_vegetables

    

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Descripción

Informática Test sobre Association and Apriori Execution Test - Machine Learning, creado por Angel Intriago el 12/12/2021.

Autor

Angel Intriago

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