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Justyna Jupowicz-Kozak

CEO of Professional Science Editing for Scientists @ prosciediting.com

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held out evaluation

Grammar usage guide and real-world examples

USAGE SUMMARY

The phrase "held out evaluation" is not correct and does not convey a clear meaning in written English.
It may be intended to refer to a type of evaluation that is offered or presented, but the wording is awkward and unclear. Example: "The committee held out evaluation results for the new program, but they were not well received."

⚠ May contain grammatical issues

Human-verified similar examples from authoritative sources

Similar Expressions

59 human-written examples

I performed five-fold cross validation, in which 20% of the known examples were held out for evaluation of classifiers trained on the remaining data, and evaluated the results by calculating receiver operating characteristic (ROC) curves averaged over the five folds.

The prediction is based on 745 field observations, of which one third is held out for evaluation, and 46 covariate layers.

Science

Geoderma

My luck held out.

News & Media

The New Yorker

CNN held out.

News & Media

The New Yorker

She held out.

News & Media

The New York Times

Mr. Obama held out.

News & Media

The New York Times

One held out.

News & Media

The New York Times

Mr. Desai held out.

News & Media

The New York Times

Only the citadel held out.

For nine months they held out.

News & Media

Forbes

A few, however, held out.

Show more...

Expert writing Tips

Best practice

When discussing evaluation in research, clarify which data subset was "held out" for validation to avoid ambiguity. For instance, specify "the model's performance was assessed using the "held out" dataset".

Common error

Avoid using the phrase "held out evaluation" without specifying what is being evaluated. Instead, use more precise language like "evaluation of the held-out data" to ensure clarity.

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

Antonio Rotolo, PhD

Digital Humanist | Computational Linguist | CEO @Ludwig.guru

Source & Trust

78%

Authority and reliability

2.8/5

Expert rating

Real-world application tested

Linguistic Context

The phrase "held out evaluation" functions as a noun phrase intended to describe a process of assessment. However, according to Ludwig AI, it is not considered grammatically correct and requires further specification to convey a clear meaning. It describes some kind of evaluation that is offered or presented.

Expression frequency: Missing

Frequent in

Science

0%

News & Media

0%

Formal & Business

0%

Less common in

Science

0%

News & Media

0%

Formal & Business

0%

Ludwig's WRAP-UP

In summary, while the phrase "held out evaluation" aims to describe an assessment process, it is considered grammatically questionable. Ludwig AI indicates it lacks clarity and requires further specification to be considered correct. More precise alternatives, such as "evaluation of the held-out data" or "assessing the held-out data", should be used to avoid ambiguity, especially in formal or scientific writing. The use of "held out" in this context typically refers to data intentionally excluded from initial analyses for validation purposes.

FAQs

How can I rephrase "held out evaluation" for better clarity?

Consider using phrases like "evaluation of held-out data", "assessing the held-out data", or "validation of reserved data" for more precision.

What does "held out" mean in the context of data analysis?

In data analysis, "held out" refers to a portion of the dataset that is intentionally excluded from the initial training or modeling process. This "held out data" is then used to independently evaluate the performance of the model.

Is "held out evaluation" grammatically correct?

While understandable, "held out evaluation" is not the most grammatically precise phrasing. It's better to use phrases like "evaluation of the held-out set" or "evaluation using held-out data".

What is the purpose of "holding out" data during model training?

The primary purpose of holding out data is to obtain an unbiased estimate of how well the model will generalize to new, unseen data. This helps prevent overfitting, where the model performs well on the training data but poorly on new data.

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Source & Trust

78%

Authority and reliability

2.8/5

Expert rating

Real-world application tested

Most frequent sentences: