What concept describes the idea that poor data quality leads to poor model outputs?

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Multiple Choice

What concept describes the idea that poor data quality leads to poor model outputs?

Garbage In, Garbage Out captures the idea that the quality of model outputs is determined by the quality of the input data. If the data fed into a model are noisy, incomplete, mislabeled, or biased, the model learns and predicts based on those flaws, yielding unreliable or biased results. This underscores the importance of data cleaning, validation, proper handling of missing values, and bias mitigation as essential steps in building trustworthy models. In contrast, overfitting describes a model learning noise from training data rather than data quality itself, imputation is a technique to fill in missing values, and exploratory data analysis is a diagnostic activity to understand and reveal data issues. The core idea here is the direct, cause-and-effect link between input data quality and output quality.

Garbage In, Garbage Out captures the idea that the quality of model outputs is determined by the quality of the input data. If the data fed into a model are noisy, incomplete, mislabeled, or biased, the model learns and predicts based on those flaws, yielding unreliable or biased results. This underscores the importance of data cleaning, validation, proper handling of missing values, and bias mitigation as essential steps in building trustworthy models. In contrast, overfitting describes a model learning noise from training data rather than data quality itself, imputation is a technique to fill in missing values, and exploratory data analysis is a diagnostic activity to understand and reveal data issues. The core idea here is the direct, cause-and-effect link between input data quality and output quality.

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