Unverified50% confidenceOpinionExact time
In Kaggle structured data competitions, winning solutions typically devote 80% of effort to feature engineering and data cleaning, and 20% to model selection and hyperparameter tuning.
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8/30/2026
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Verified80% of the success or failure of fine-tuning depends on data quality, and data preparation typically accounts for 60%–70% of the total project workload.68% similarUnverified在实际工业环境中，数据理解、特征工程、评估指标选择、模型监控和迭代往往占据80%以上的工作量，模型选择只是很小的一环62% similarUnverified靶向数据工程中，针对性数据占总微调数据的 5%-15% 通常能取得良好效果，过高比例可能导致模型在其他维度退化61% similarUnverifiedIn practice, modeling may account for only 20% of an ML engineer's workload, with the remaining 80% devoted to data pipelines, feature engineering, deployment and operations, and iterative optimization.60% similarVerifiedIn actual machine learning projects, data preprocessing typically accounts for 60%–80% of the workload.60% similar
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