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Data Analytics (Eng) / Data Analitika (Ing) - 344

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Why is Pearson correlation used rather than covariance?
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 Consider a heatmap where each row and column represent a feature, and the elements of the heatmap represent the Pearson correlation between two features:

 Feature1Feature2Feature3Feature4Feature5Feature6Feature7Feature8Feature9Feature10
Feature110,20,5-0,10,3-0,20,40,6-0,30,1
Feature20,21-0,40,7-0,50,3-0,60,20,8-0,8
Feature30,5-0,41-0,30,20,6-0,5-0,20,10,3
Feature4-0,10,7-0,31-0,60,20,5-0,70,4-0,5
Feature50,3-0,50,2-0,61-0,40,30,5-0,20,6
Feature6-0,20,30,60,2-0,41-0,30,40,5-0,1
Feature70,4-0,6-0,50,50,3-0,31-0,40,20,7
Feature80,60,2-0,2-0,70,50,4-0,41-0,30,6
Feature9-0,30,80,10,4-0,20,50,2-0,31-0,4
Feature100,1-0,80,3-0,50,6-0,10,70,6-0,41

Given that Feature10 is the target feature which feature is the most informative?

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Consider a heatmap where each row and column represent a feature, and the elements of the heatmap represent the Pearson correlation between two features:

 Feature 1Feature 2Feature 3Feature 4
Feature 110,75-0,30,1
Feature 20,810,6-0,2
Feature 3-0,20,510,8
Feature 40-0,10,951

 

Does the provided heatmap contain any error?

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What does covariance measure?
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Which of the following best describes an irrelevant feature?
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Which of the following best describes the filter method of feature selection?
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In an ABT for customer churn analysis, each row would most likely represent:
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How can data staleness negatively impact an ABT used for customer behavior analysis?
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Which of the following derived features can be created from a "Birthdate" column to improve a demographic model?
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What detail level do the rows in an Analytic Base Table (ABT) typically capture?
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