DA0-001 · Question #337
A data analyst needs to apply quality control concepts to a data set for accuracy. Which of the following is the best way to do this?
The correct answer is D. Cross-validation. To ensure the accuracy and generalization ability of a data model, cross-validation is a crucial quality control technique that assesses how well the model performs on unseen data. It helps identify overfitting and provides a more robust estimate of model performance.
Question
A data analyst needs to apply quality control concepts to a data set for accuracy. Which of the following is the best way to do this?
Options
- AStandardization
- BParameterization
- CEncryption
- DCross-validation
How the community answered
(28 responses)- A4% (1)
- C4% (1)
- D93% (26)
Why each option
To ensure the accuracy and generalization ability of a data model, cross-validation is a crucial quality control technique that assesses how well the model performs on unseen data. It helps identify overfitting and provides a more robust estimate of model performance.
Standardization involves transforming data to a common scale, which aids in model training but does not directly measure overall data accuracy or model generalization.
Parameterization involves defining parameters for a model or process, but it is not a direct method for quality control of data accuracy.
Encryption is a security measure to protect data confidentiality, not a method for assessing data accuracy or model performance.
Cross-validation is a quality control technique used to assess the accuracy and robustness of a model by dividing the dataset into multiple subsets for training and testing. This process helps ensure that the model performs well on new, unseen data, which is critical for data accuracy and generalization.
Concept tested: Data quality control, model accuracy, cross-validation
Source: https://learn.microsoft.com/en-us/azure/machine-learning/concept-cross-validation
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