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Getting good predictions without data cleaning (Why "Garbage In, Garbage Out" is sometimes a trap)
Full arXiv Preprint: https://arxiv.org/abs/2603.12288
Paper Simulation Github: https://github.com/tjleestjohn/from-garbage-to-gold
A brief note on the paper:
We recently released a preprint arguing that treating "Garbage In, Garbage Out" (GIGO) as a universal law can sometimes be a trap… especially in the context of big data. Despite this, our field is still bottlenecked with endless manual cleaning and aggressive imputation to curate pristine, error-free tables.
Why do we need to rethink GIGO?
The traditional mindset often conflates two different types of "noise": Predictor Error, which is random typos, dropped logs, or transient glitches, and Structural Uncertainty, the inherent gap between recorded metrics and the complex, hidden reality they represent. Manual cleaning can fix errors but not structural uncertainty.
The practical problem with manual cleaning:
To overcome Structural Uncertainty, modern AI/ML models need a high-dimensional set of variables that contains Informative Collinearity. However, introducing manual cleaning creates a bottleneck. By artificially restricting the predictor space to make it "clean enough to model," we can harm the data architecture’s potential to triangulate hidden drivers.
Solution: High-dimensional data architecture and flexible models
We argue that instead of focusing on manual cleaning, we should focus mostly on extracting, loading, and increasing observational fidelity with automated tools. In contexts characterized by latent drivers, this approach can save time and enhance the predictive ceiling of our tabular AI/ML models.
Key Takeaways
- We need to distinguish between different types of noise in data: Predictor Error (random issues) and Structural Uncertainty (the gap between recorded metrics and reality).
- Manual cleaning can fix errors but not structural uncertainty.
- A high-dimensional data architecture with flexible models can help overcome Structural Uncertainty without the need for extensive manual cleaning.
- We should prioritize automated tools to extract and load data, rather than relying on manual cleaning as a bottleneck.
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Originally published at reddit.com. Curated by AI Maestro.
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