A retailer wants to build a demand-forecasting model but discovers that its historical sales data contains many missing values and duplicate records. From a machine-learning perspective, why is it critical to fix these data quality issues before training begins?
Models learn patterns present in the training data, so inaccurate or inconsistent data will lead the model to make unreliable forecasts in production.
Using cleaner data eliminates the need for specialized compute hardware such as GPUs during model training.
Regulations require all machine-learning datasets to be perfectly balanced, so correcting data issues is mandatory for legal compliance.
Cloud Storage applies higher charges to datasets that contain errors, so cleaning the data avoids extra cost.
Machine-learning algorithms learn statistical patterns that exist in the data they are given. If the training data contains errors, gaps, or inconsistencies, the model will learn those flaws and propagate them to its predictions, lowering accuracy and trust. Clean, accurate, and representative data enables the model to detect the true relationships that matter for forecasting, while poor-quality input ("garbage in") produces unreliable output ("garbage out"). Storage cost, hardware choice, or generic compliance rules do not address the root cause of model performance problems-data quality does.
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