mean absolute error

USAGE SUMMARY

'mean absolute error' is a correct and usable term in written English.
It is typically used in statistics when measuring the accuracy of predictions. For example, "The mean absolute error of our model's predictions was 2.5."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

For each case, we chose to use the model with the smallest mean absolute error (MEAN).

Human Resources for Health

In order to show error levels, mean absolute error and mean absolute error percentage are used.

Energy Procedia

Mean absolute error and mean absolute error percentages are very common and practical methods in literature.

Energy Procedia

The model has mean absolute error (MAE) of 4.29 years.

Computer Methods and Programs in Biomedicine

(7) Note that the mean absolute error may be degraded without the correlation being degraded.

Columbia University

The capability of candidates to predict dose was compared using mean absolute error (MAE) of predicted dose vs. actual dose.

Nature

These accounted for approximately 50% of the total variation with mean absolute error ± 5 ppb.

Science of The Total Environment

The mean percentage error (mean absolute error/ maximum experimental force) for these predictions was 7±3%.

Journal of the Mechanical Behavior of Biomedical Materials

In all cases the mean absolute error did not exceed 12%.

Advances in Water Resources

The accuracy of the prediction (mean absolute error ≤2 mm) was 87%.

International Journal of Oral and Maxillofacial Surgery

Coefficient of determination (CoD) and mean absolute error (MAE) were taken as performance measures.

International Journal of Mining Science and Technology

Expert writing Tips

Best practice

When reporting the "mean absolute error", always specify the units of measurement to provide context for the magnitude of the error. For instance, "The model has a mean absolute error of 4.29 years".

Common error

Avoid using "mean absolute error" interchangeably with other error metrics like RMSE (root mean squared error). RMSE penalizes larger errors more heavily, making it suitable for different situations.

Linguistic Context

The phrase "mean absolute error" functions as a noun phrase, specifically as a statistical term. It quantifies the average magnitude of errors in a set of predictions, without considering their direction. Ludwig AI confirms its correct usage, evidenced by numerous examples.

Expression frequency: Very common

Frequent in

Science

94%

Academia

5%

Formal & Business

1%

Less common in

News & Media

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "mean absolute error" (MAE) is a widely used statistical term representing the average magnitude of errors in predictions, disregarding their direction. Ludwig AI analysis confirms its correct and frequent usage, especially within scientific and academic contexts. It's important to specify the units of measurement alongside the MAE value for context. While similar to other error metrics like RMSE, MAE is less sensitive to outliers. When communicating the accuracy of models, using alternatives like "average absolute deviation" can provide variety, although "mean absolute error" is the most conventional choice.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

average absolute deviation

This alternative replaces "mean" with "average" and "error" with "deviation", maintaining the core meaning of an average magnitude of difference.

mean absolute deviation

Replaces "error" with "deviation", offering a slightly different wording while conveying the same statistical concept.

average absolute difference

This alternative emphasizes the difference between values and takes the absolute value before averaging.

l1 norm error

This is a more technical term, referring to the mathematical concept underlying the mean absolute error, specifically the L1 norm which calculates the sum of absolute values.

average error magnitude

This phrase focuses on the magnitude of the error on average, similar to mean absolute error but without explicitly stating the absolute value operation.

magnitude of average error

This expresses the average error's size without regard to its direction, synonymous with "mean absolute error".

average of absolute residuals

This phrase highlights that the errors are calculated as residuals (the difference between observed and predicted values) and then averaged.

absolute loss

In machine learning, absolute loss refers to the same concept as the mean absolute error but focuses on the loss function applied to individual predictions.

expected absolute error

Highlights that we are discussing the anticipated average absolute difference, rather than a realized one from a particular dataset.

mean rectified error

Replaces "absolute" with "rectified", referring to the process of converting negative values to positive ones, mirroring the effect of absolute value.

FAQs

What does "mean absolute error" tell you?

The "mean absolute error" (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction. It indicates how close the predictions are to the actual values, on average. A lower MAE suggests better model accuracy.

How is "mean absolute error" different from mean squared error?

While both "mean absolute error" and mean squared error (MSE) quantify prediction errors, MAE calculates the average absolute difference between predictions and actual values, whereas MSE squares these differences before averaging. Squaring gives more weight to larger errors, making MSE more sensitive to outliers than MAE.

When should I use "mean absolute error" versus root mean squared error?

Use "mean absolute error" when you want a measure of the average magnitude of errors that is less sensitive to outliers. Use root mean squared error (RMSE) when you want to penalize larger errors more heavily and outliers are important to consider.

What are some alternatives to "mean absolute error"?

Alternatives to "mean absolute error" include "median absolute error", which is even more robust to outliers, and "root mean squared error", which emphasizes larger errors. The choice depends on the specific application and the importance of outliers.