mean residual error

USAGE SUMMARY

The phrase "mean residual error" is correct and usable in written English.
It is typically used in statistical analysis and machine learning to describe the average of the differences between predicted and actual values. Example: "The model's performance was evaluated using the mean residual error, which indicated how well the predictions matched the actual outcomes."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

23 human-written examples

The parameters used in this study for performance evaluation are absolute residual (AR), mean of absolute residual (MAR), standard deviation of absolute residual (SDAR), mean residual error (MRE), and mean of mean residual error (MMRE) [31, 32].

EURASIP Journal on Image and Video Processing

Varying the parameters ρ1 – ρ3 in Eq. (2) to be representative of HG (g = [0.5 – 0.9]) and MHG (g = [0.9]) phase functions (see Table 1) induced only a 2.2% mean residual error between R SF meas – abs (based on the average ρ1 – ρ3) and R SF Model (based on the different ρ1 – ρ3 combinations listed in Table 1).

Biomedical Optics Express

Simulated output for 1998 and 1999 were compared to measured values based on the mean residual error (MRE), coefficient of efficiency (Ceff=1.0 represents a perfect match; Ceff≤0 a poor match) and other statistical parameters.

Agricultural Water Management

Using a combination of integral long-profile analysis and stream-power modelling, we find that the locations of ∼80% of knickpoints in our study region are consistent with that predicted for a fluvial origin, while the mean residual error over ∼100 km of modelled channels is just 26.3 m.

Earth and Planetary Science Letters

Mean residual error.

EURASIP Journal on Image and Video Processing

Mean of mean residual error.

EURASIP Journal on Image and Video Processing

Human-verified similar examples from authoritative sources

Similar Expressions

35 human-written examples

For all of the datasets processed with Agilent Feature Extraction software only, the mean residual errors from using the Imin pooling metric are always less than or equal to the corresponding mean residual errors from using the Iavg pooling metric.

BMC Genomics

The trends in the mean residual errors from the unpaired reference sample analysis agree with the results from the direct comparison analyses.

BMC Genomics

The mean residual errors for this analysis are shown in Fig. 8. Here, combinations of small fiber diameters are associated with increased error; this trend is substantially increased for measurements in the low scattering regime (set 1).

Biomedical Optics Express

The data presented here show that the mean residual errors are either equal or lower when using the Imin compared to the Iavg pooling metric, for every dataset using the Agilent Feature Extraction image processing technique.

BMC Genomics

For all three cases both μ′ s and γ were accurately estimated from the whole dataset, with mean residual errors of ≤ 5% for μ′ s and of ≤ 3% for γ.

Biomedical Optics Express

Expert writing Tips

Best practice

When reporting the "mean residual error", always specify the units of measurement and the context of the data. This ensures clarity and reproducibility of your results.

Common error

Avoid using "mean residual error" interchangeably with root mean squared error (RMSE) or mean absolute error (MAE). Each metric provides different insights into the distribution and magnitude of errors.

Linguistic Context

The phrase "mean residual error" functions as a noun phrase that names a specific statistical measure. It's used to quantify the average difference between observed and predicted values in a model. Ludwig AI confirms this is a valid phrase.

Expression frequency: Common

Frequent in

Science

98%

Academia

1%

Formal & Business

1%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "mean residual error" is a common and grammatically correct term used extensively in scientific and statistical contexts to evaluate the accuracy of predictive models. Ludwig AI validates its proper usage. It represents the average difference between predicted and actual values, providing a key metric for assessing model fit. While alternatives like "average error" or "mean prediction deviation" exist, "mean residual error" remains the standard in technical reporting. Key considerations include specifying units of measurement and avoiding confusion with similar metrics like RMSE or MAE. Its frequent use in science underscores its importance in quantitative analysis.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

average of residual errors

This restructures the phrase to emphasize that it's an average calculated from multiple errors.

average error

This alternative uses "average" instead of "mean", offering a simpler term for the same statistical concept.

mean error in residuals

Clarifies that the error pertains to residuals, slightly rephrasing the original.

average prediction error

This specifies that the error relates to predictions, which can be helpful in clarifying the context.

mean prediction deviation

This uses "deviation" instead of "error", changing the nuance to focus on how much predictions deviate.

mean discrepancy

Using "discrepancy" as a replacement for "error" offers a slightly different connotation, emphasizing the difference between values.

average deviation from expected

This rephrases the concept to highlight the difference from expected values, adding clarity.

typical difference from predicted values

This alternative focuses on the difference from prediction, providing a more descriptive phrase.

residual average

This inverts the order and uses a slightly less common term.

typical residual error

Replaces "mean" with "typical", suggesting a usual or common error amount.

FAQs

What does "mean residual error" indicate?

The "mean residual error" indicates the average difference between predicted and actual values in a dataset. It provides a measure of how well a model fits the data, with a lower value indicating a better fit.

How is "mean residual error" calculated?

The "mean residual error" is calculated by summing the differences (residuals) between predicted and actual values, and then dividing by the number of data points. The formula is: MRE = (∑(actual - predicted)) / n, where n is the number of observations.

What are some alternatives to "mean residual error"?

You can use alternatives like "average error", "average prediction error", or "mean prediction deviation" depending on the context.

What's the difference between "mean residual error" and mean absolute error?

"Mean residual error" can be positive or negative, indicating over or under-prediction, while mean absolute error always returns a positive value by taking the absolute value of each residual before averaging. This makes mean absolute error useful when magnitude of the error is more important than its direction.