Used and loved by millions
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.
Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com
be robust to data
Grammar usage guide and real-world examplesUSAGE 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
Alternative expressions(4)
Table of contents
Usage summary
Human-verified examples
Expert writing tips
Linguistic context
Ludwig's wrap-up
Alternative expressions
FAQs
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.
Science
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.
Science
This model is robust to data missing at random and can also take the covariance structure of the data into account [ 43].
Science
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)].
Science
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].
Science
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.
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.
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.
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.
More alternative expressions(6)
Phrases that express similar concepts, ordered by semantic similarity:
be resilient to data
Replaces "robust" with "resilient", emphasizing the ability to recover quickly from difficulties.
be resistant to data
Substitutes "robust" with "resistant", highlighting the capacity to withstand the effects of data-related problems.
be insensitive to data
Changes "robust" to "insensitive", focusing on the lack of reaction to data variations.
be immune to data
Replaces "robust" with "immune", suggesting a complete protection against data-related issues.
be unaffected by data
Uses a passive construction to emphasize the lack of impact from data-related problems.
be stable against data
Highlights the stability of a system when exposed to data
maintain integrity with data
Focuses on preserving data integrity even with noisy information.
perform reliably despite data
Emphasizes reliable performance, even when considering underlying information.
tolerate noisy data
Specifies that the phrase is able to tolerate noisy data and perform normally.
withstand data errors
Shows the capacity to withstand data errors and problems.
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".
Editing plus AI, all in one place.
Stop switching between tools. Your AI writing partner for everything—polishing proposals, crafting emails, finding the right tone.
Table of contents
Usage summary
Human-verified examples
Expert writing tips
Linguistic context
Ludwig's wrap-up
Alternative expressions
FAQs
Source & Trust
83%
Authority and reliability
4.5/5
Expert rating
Real-world application tested