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Justyna Jupowicz-Kozak

CEO of Professional Science Editing for Scientists @ prosciediting.com

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be robust to data

Grammar usage guide and real-world examples

USAGE SUMMARY

The phrase "be robust to data" is correct and usable in written English.
It can be used in contexts discussing the resilience or reliability of a system, model, or process in relation to varying data inputs. Example: "The algorithm must be robust to data variations to ensure accurate predictions across different scenarios."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

3 human-written examples

However, a good feature selection method should be robust to data variation.

At last, practical fusion algorithms should be robust to data errors due to channel impairment, interference, and noise.

Thus, we design our GPs covariance matrix via the proposed ℓ1 construction and a local approximation (LA) covariance weight updating method, which are demonstrated to be robust to data noise, automatically sparse and adaptive to the neighborhood.

Human-verified similar examples from authoritative sources

Similar Expressions

57 human-written examples

Because the data for moral leadership preference is skewed to the left with most of the respondents showing a high preference for moral leadership, an MLR estimator is used which is robust to data non-normality.

This survival network is robust to data removal and is statistically significant as estimated under data randomisation.

This model is robust to data missing at random and can also take the covariance structure of the data into account [ 43].

A high correlation of CPA between the two datasets is found across chromosomes, implying that CPA is robust to data from different genotyping sites [Additional file 5, Supplemental Figure S5 (B)].

Note that the gene tree parsimony approach used by DupTree is expected to be robust to missing data (e.g. from incomplete transcriptomic data), whereas the split fit approach used by Clann is more sensitive [ 74, 78].

This approach proves to be robust to noisy data.

Most existing solutions are based on learning algorithms that are designed to be robust to missing data.

The model proved to be robust to new data.

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Expert writing Tips

Best practice

When describing algorithms or models, use "be robust to data" to indicate their ability to function correctly despite noisy, incomplete, or varying input data.

Common error

Avoid using "be robust to data" when you actually mean the data itself is robust. "Be robust to data" describes a quality of a system or process, not the data itself.

Antonio Rotolo, PhD - Digital Humanist | Computational Linguist | CEO @Ludwig.guru

Antonio Rotolo, PhD

Digital Humanist | Computational Linguist | CEO @Ludwig.guru

Source & Trust

83%

Authority and reliability

4.5/5

Expert rating

Real-world application tested

Linguistic Context

The phrase "be robust to data" functions as a descriptive adjective phrase indicating a quality of a system or method. As Ludwig AI confirms, it describes the capability of remaining effective despite variations or imperfections in the data. The examples show it is used to describe feature selection methods, fusion algorithms and models.

Expression frequency: Rare

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

In summary, the phrase "be robust to data" is a grammatically sound and technically relevant phrase predominantly used in scientific contexts. As Ludwig AI indicates, it effectively conveys the capacity of a system or model to maintain its effectiveness despite noisy or imperfect data. While relatively rare, its usage is consistent within its specific domain, emphasizing reliability and stability. Alternatives like "be resilient to data" offer nuanced variations in meaning, and understanding its scope helps prevent misapplication. When writing, ensure you're describing a system's quality, not the data itself. Overall, "be robust to data" is a precise and valuable term in technical and scientific writing.

FAQs

How can I use "be robust to data" in a sentence?

You can say, "The algorithm needs to "be robust to data" variations to ensure accurate predictions."

What does it mean for a model to "be robust to data"?

It means the model can maintain its performance and reliability even when the input "data" is noisy, incomplete, or contains errors.

Which is a better phrase, "be robust to data" or "be resilient to data"?

Both phrases are similar, but ""be robust to data"" emphasizes the ability to withstand or resist the effects of data variations, while "be resilient to data" emphasizes the ability to recover quickly from any negative impact.

What is the context where "be robust to data" is commonly used?

The phrase "be robust to data" is frequently encountered in scientific and technical contexts, where the reliability of algorithms and models is critical in the presence of variable or imperfect "data".

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Real-world application tested

Most frequent sentences: