auroc

USAGE SUMMARY

The part of the phrase "auroc" is correct and usable in written English, particularly in specific contexts like statistics or machine learning.
You can use it when discussing the area under the receiver operating characteristic curve, which is a performance measurement for classification models. Example: "The model achieved an AUROC of 0.85, indicating good predictive performance."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

The comparison of the AUC indicated that the total cell death marker is significantly different from the apoptotic marker (total cell death marker AUROC versus apoptotic marker AUROC: P = 0.0006; total cell death marker AUROC versus necrotic marker AUROC: P = 0.73; apoptotic marker AUROC versus necrotic marker AUROC: P = 0.0505).

Plosone

Urine NGAL had a fair predictive value on admission (AUROC 0.79) and at 24 hours (AUROC 0.78) and was good at 48 hours (AUROC 0.82).

Critical Care

Unfortunately, in these cases, high AUROC values do not imply high precision.

Nature

This late model demonstrated AUROC of 0.891.

Annals of Intensive Care

The AUROC curve was, however, not estimated.

Critical Ultrasound Journal

This early model demonstrated AUROC of 0.766.

Annals of Intensive Care

c AUROC values for the individual drug-target models.

Journal of Cheminformatics

Larger AUROC values correspond to better performance.

Bioinformatics

The AUROC curve was 0.88 [ 21].

BMC Geriatrics

Here, we measure the auROC as the generalization performance (random guessing corresponds to 50% auROC).

Bioinformatics

The areas-under-the-ROC-curves (AUROC) as well as 95%-CI of AUROC were calculated.

BMC Gastroenterology

Expert writing Tips

Best practice

When reporting "auroc" values, always specify the confidence interval to provide a measure of the estimate's precision. For example, "auroc 0.85 (95% ci: 0.80-0.90)".

Common error

Don't assume a high "auroc" automatically implies a clinically useful model. Consider the prevalence of the condition being predicted; a high "auroc" can be misleading if the condition is rare.

Linguistic Context

The term "auroc" functions as a noun, specifically an abbreviation standing for area under the receiver operating characteristic curve. It is primarily used to quantify the performance of classification models in statistics and machine learning. As Ludwig AI highlights, higher values typically indicate better performance.

Expression frequency: Very common

Frequent in

Science

98%

Academia

1%

Formal & Business

1%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "auroc" is a very common abbreviation for the area under the receiver operating characteristic curve, used to evaluate the performance of classification models. As Ludwig AI explains, this term is grammatically correct and mostly used in scientific contexts. It is interpreted as a measure of the model's ability to discriminate between different classes, with values closer to 1 indicating better performance. When using or interpreting "auroc", it's essential to consider confidence intervals and the prevalence of the condition being predicted to avoid misinterpretations. The related phrases offer alternative ways to describe similar concepts. Reporting "auroc" is vital in academic papers and research for clearly communicating the effectiveness of models. It is also important to remember that despite its importance it's not the only metric that describes a statistical model.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

area under the receiver operating characteristic curve

This is the full form of the abbreviation "auroc", providing a complete and unambiguous description of the statistical measure.

auc

Another abbreviation for "area under the curve", often used interchangeably with "auroc".

c-statistic

Represents a measure of discrimination, especially within medical contexts. It quantifies how accurately a model can distinguish between two outcome groups.

discrimination ability

This describes the power of a predictive model to accurately differentiate between cases. Unlike "auroc" which is a quantified number, this alternative refers to a qualitative feature.

predictive accuracy

This general phrase reflects how well a model's predictions align with actual results, similar to what "auroc" assesses.

model performance

A more general term indicating how well a model operates, including various aspects such as accuracy, precision, and recall.

classification metric

A broad category that "auroc" falls into, referring to any measure used to evaluate the performance of classification models.

receiver operating characteristic

Referring to the complete curve, rather than the area under it. "Auroc" quantifies the area under the curve.

goodness of fit

Evaluating how well a statistical model represents the observed data. It may not necessarily imply the discrimination ability, as "auroc" does.

performance evaluation

A higher-level term applicable to many contexts. Unlike "auroc", this one does not relate exclusively to model's performances.

FAQs

What does "auroc" stand for?

"Auroc" stands for area under the receiver operating characteristic curve. It is a measure of the ability of a classification model to distinguish between classes.

How is "auroc" interpreted?

An "auroc" of 0.5 suggests no discrimination, while an "auroc" of 1.0 indicates perfect discrimination. Values between 0.7 and 0.8 are considered acceptable, 0.8 to 0.9 are considered excellent and >0.9 is considered outstanding.

What is the difference between "auroc" and "auprc"?

"Auroc" (area under the receiver operating characteristic curve) and "auprc" (area under the precision-recall curve) are both measures of classification performance. "Auroc" is better for balanced datasets, while "auprc" is more sensitive to imbalanced datasets.

What are some alternatives to reporting "auroc"?

Besides reporting the "auroc" value, you can also report the sensitivity and specificity at a specific threshold, or provide the full receiver operating characteristic curve. You can also refer to "c-statistic".