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When a machine-learning model underperforms, check whether its data and evaluation match the real task before spending more time tuning. Incorrect labels, weak features, missing edge cases, or an unrepresentative test set can all obscure what the model can actually do. That is a troubleshooting heuristic, not a rule that data work always matters more: data and model improvements are complementary, and both should be judged against the same task-relevant evaluation.
What “fix the data” means
Data-centric AI is the systematic design and engineering of data; it is not simply adding more examples. A 2024 review distinguishes data refinement—making existing data better—from data extension—adding data. It also argues that both data quality and quantity matter. The review’s framework focuses on supervised machine learning, while noting that data-centric methods also apply to unsupervised and reinforcement learning.
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Refine what you already have
Refinement can mean correcting labels or features, finding low-quality or duplicate examples, and improving coverage of relevant edge cases. The goal is not to make the dataset look tidier at any cost: an unusual example may be a valid and important case, not bad data. Distinguishing the two can require domain expertise and semi-automated tools.
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Extend to address blind spots
Extension can add observations, features, or labels when the existing data misses part of the task, population, or distribution. More examples help only when they contribute useful coverage; they do not automatically correct systematic label errors or an evaluation that fails to represent deployment.
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First check whether the evaluation tests the real task
A model score is only as useful as the test set behind it. A random test split drawn from the same pool as training data can measure performance on that sampled pool without showing whether the model solves the underlying deployment problem. Google Research’s DataPerf overview warns that test sets drawn from the same pool can conflate fitting that data well with solving the real problem.
Before comparing fixes, check whether the evaluation reflects the deployment population, time period, and important subgroups. A test set designed for one distribution may not answer how the model behaves after that distribution shifts. DataPerf’s NeurIPS 2023 paper describes a first iteration with five benchmarks spanning data-centric techniques and modalities; it offers a framework for studying data-focused improvements, not a guarantee that one evaluation design fits every application. Read the DataPerf benchmark paper.
A practical way to diagnose an underperforming model
- Define the deployment task and success measure. Specify what the model must do and how success will be measured. Check that the test data represents the real population, time period, and important subgroups rather than assuming a random split is sufficient.
- Profile training and test data. Look for label errors, duplicates, low-quality examples, missing or inaccurate features, and underrepresented cases relevant to the task. Treat outlier flags as prompts to investigate, not automatic deletion instructions.
- Prioritize review where it can change an outcome. Ask domain experts to resolve ambiguous labels and assess rare cases. Because manual review is limited, focus it on examples where an error or omission matters most to the task.
- Change one thing at a time where practical. Version the dataset and track the model and data versions. Compare each change against the same evaluation so you can interpret whether the difference came from the data change.
- Investigate the model when the data checks are sound. If the task, data quality, and coverage are adequate, examine model selection, architecture, and hyperparameters. Data-centric and model-centric work are distinct approaches, but the 2024 review describes them as complementary parts of effective AI development.
Choose the next investment by the likely failure source
Use the evidence from evaluation and data profiling to decide what deserves the next unit of effort. Data work is a stronger next step when there is a plausible data problem; model work is more promising when the task is well represented and the remaining issue points to model choice or capacity.
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- Likely failure source: Are labels, features, or coverage suspect, or is there evidence the model lacks suitable capacity?
- Evaluation fidelity: Does the test set represent the actual task and its distribution?
- People and cost: Are domain experts available to adjudicate cases, and what would annotation or review cost?
- Compute and engineering: Compare the cost of preparing data with the cost of additional model experiments.
- Consistency across conditions: Check whether an apparent gain holds across relevant time windows and groups. This is a practical way to account for distribution shifts, not a quantified result established by the cited studies.
What published results do—and do not—show
A 2024 image-classification study reports that its data-centric approach improved results by at least 3% in the authors’ tested ResNet-18 experiments on MNIST, Fashion MNIST, and CIFAR-10. The methods included duplicate removal, noisy-label correction, and augmentation. That is a study-specific result, not a forecast of the gain another team should expect. See the Scientific Reports paper.
A 2025 tabular-data study examined 19 machine-learning algorithms and six data-quality dimensions across classification, regression, and clustering. Those numbers describe the study’s scope, not an effect size; they do not establish how much a particular data fix will improve another project. See the Information Systems paper.
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