What technique would be used to ensure model accuracy in a data set?

Choosing data cleaning and validation is essential for ensuring model accuracy in a dataset because these processes directly influence the quality of the input data used for modeling.

Data cleaning involves identifying and correcting errors or inconsistencies in the data. This can include handling missing values, removing duplicates, and correcting erroneous entries. By ensuring that the dataset accurately reflects the real-world phenomena it aims to model, the resulting insights and predictions become more reliable.

Validation checks the integrity and accuracy of the data. This step is crucial because it verifies whether the data meets the necessary standards for analysis, which can include ensuring data types are correct, values fall within expected ranges, and that the data can be trusted to represent what it is supposed to depict.

In contrast, other options like data collection only focus on gathering data without addressing its quality. Random sampling of data is useful for reducing data size or creating testing datasets but does not inherently improve data quality itself. Data compression methods aim to reduce storage costs or improve processing times but do not contribute to ensuring the accuracy or reliability of the data. Thus, cleaning and validating data are critical steps in creating a robust and accurate data model.