roc value

USAGE SUMMARY

The phrase "roc value" is not standard in written English and may not be widely recognized.
It could be used in specific contexts, such as finance or data analysis, where "ROC" stands for "Return on Capital" or "Rate of Change," but clarity is essential. Example: "The roc value indicates the efficiency of the investment over time."

Science

Human-verified examples from authoritative sources

Exact Expressions

49 human-written examples

ROC gd is the ROC value of gene, g, of disease, d, and n d is the number of samples in disease, d.

EMBO Molecular Medicine

These values are then averaged over the entire dataset to obtain a ROC value for a given epigenetic feature versus an integration site dataset (see Supplementary statistical analysis for complete lists of ROC and P values).

Plosone

Validations through different statistics have been also done, and ROC value of 89.5% was achieved.

Environmental Systems Research

For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI.

Journal of Environmental Management

Equation 7 represents the interrelation between the ROC value and the ROC enrichment for a predefined false positive fraction.

Journal of Cheminformatics

In every fold of the cross validation, the best set of 20 channels is chosen based on the ROC value.

Plosone

Human-verified similar examples from authoritative sources

Similar Expressions

11 human-written examples

High ROC values, over 0.8, were achieved with the Mahalanobis typicality images of both mature and young rubber trees.

Applied Geography

The respective significant AUC ROC values are printed in bold in Table 2.

Journal of Cheminformatics

The AUC ROC values for the different sets of curves vary notably.

Journal of Cheminformatics

The reader is referred to Miller et al. (2016) for the individual ROC values for the Asia-Pacific.

Journal of Applied Volcanology

The set of curves ascending steeper at the beginning belongs to the confidence measures and yield larger AUC ROC values.

Journal of Cheminformatics

Expert writing Tips

Best practice

When reporting the "roc value", always specify whether it's an area under the curve (AUC) to provide a clear and complete understanding of the metric.

Common error

Avoid assuming that a higher "roc value" automatically indicates a practically useful model; consider the context and the specific application to ensure the improvement is meaningful and not just statistically significant.

Linguistic Context

The phrase "roc value" primarily functions as a statistical descriptor. It quantifies the performance of a binary classification model, indicating its ability to discriminate between two classes. Ludwig provides numerous examples demonstrating its use in scientific contexts.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

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Formal & Business

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Encyclopedias

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Ludwig's WRAP-UP

The term "roc value" is a common metric in scientific and statistical contexts, used to assess the performance of binary classification models. Despite Ludwig AI suggesting it may not be standard in all English writing, its prevalence in scientific literature makes it an acceptable and important term in that domain. It's crucial to specify that the "roc value" often refers to the area under the ROC curve (AUC) for clarity. Alternatives like "AUC ROC score" or "discrimination statistic" can be used depending on the context. When interpreting or reporting the "roc value", consider its magnitude in relation to the specific application to ensure the improvements are meaningful.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

auc roc score

This alternative specifies that the ROC value is derived from the area under the curve, providing additional clarity.

roc auc value

This alternative reorders the acronym and the full name, emphasizing the value itself. Is also a common way to refer to it.

receiver operating characteristic value

This expands the acronym to its full form, enhancing clarity for readers unfamiliar with the abbreviation.

area under the roc curve

This describes the ROC value as an area under a curve, specifying the method of calculation.

area under curve roc

This reorders the phrase but conveys the same meaning as "area under the ROC curve".

roc metric

This substitutes "value" with "metric", a more general term for a measure or standard.

performance statistic

This replaces "roc value" with a broader term indicating a statistical measure of performance.

discrimination statistic

This focuses on the ROC value as a measure of discrimination ability.

predictive accuracy

This is a more general term referring to the accuracy of a model's predictions, which the ROC value assesses.

model performance

This refers to the overall effectiveness of a model, of which the ROC value is one indicator.

FAQs

How is the "roc value" interpreted in model evaluation?

The "roc value", or Receiver Operating Characteristic value, typically ranges from 0.5 to 1. A value of 0.5 indicates no discrimination ability, while a value of 1 indicates perfect discrimination. Values above 0.7 are generally considered acceptable, and values above 0.8 are considered good.

What is the difference between the "roc value" and the AUC?

While often used interchangeably, the "roc value" is technically derived from the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The AUC summarizes the performance of the classifier across all possible thresholds, providing a single scalar value representing the overall performance.

What are some alternatives to using the "roc value" for model assessment?

Besides the "roc value", you can use metrics such as precision, recall, F1-score, and the Brier score to assess model performance, depending on the specific goals and characteristics of your data. Also the use of "AUC ROC score" is an alternative.

How do I improve a model with a low "roc value"?

If your model has a low "roc value", consider feature engineering, trying different algorithms, or adjusting the decision threshold. Also, check for class imbalance and address it with techniques like oversampling or undersampling. You can also try to use a "discrimination statistic" as an alternative.