A data science team is running a series of experiments to compare different feature engineering techniques. They want to ensure that any performance differences are due to the features, not the data split. What should they do?

Use the same fixed random seed for all splits

Use a different random seed for each experiment

Increase the size of the test set

Shuffle the data manually before each split

CertNexusCertified Data Science Practitioner (CDSP)

Domain 4Objective 1

Objective 4.1 Prepare Data Sets for Modeling CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 6)

Part of the 4.0 Building models domain, which accounts for 19-27% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

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Questions 26–26

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A data science team is running a series of experiments to compare different feature engineering techniques. They want to ensure that any performance differences are due to the features, not the data split. What should they do?
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Objective 4.1 Prepare Data Sets for Modeling CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 6)

Questions 26–26

A data science team is running a series of experiments to compare different feature engineering techniques. They want to ensure that any performance differences are due to the features, not the data split. What should they do?

Keep studying — 4.0 Building models