mean square error

USAGE SUMMARY

The phrase "mean square error" is correct and usable in written English.
It is typically used in statistics and machine learning to quantify the difference between values predicted by a model and the actual values. Example: "The mean square error of the model indicates how well it predicts the target variable."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

53 human-written examples

mean square error regression.

Fashion and Textiles

Excess mean square error.

EURASIP Journal on Advances in Signal Processing

Weighted mean square error.

EURASIP Journal on Advances in Signal Processing

Normalised mean square error.

International Journal of Energy and Environmental Engineering

Averaged root mean square error.

EURASIP Journal on Wireless Communications and Networking

Normalised mean square error (NMSE).

International Journal of Energy and Environmental Engineering

Human-verified similar examples from authoritative sources

Similar Expressions

7 human-written examples

Root-mean square error (RMSE).

Renewables: Wind, Water, and Solar

Normalized mean-square error.

EURASIP Journal on Advances in Signal Processing

Root-mean-square error.

Earth, Planets and Space

b Root-mean-square error.

Journal of Petroleum Exploration and Production Technology

Relative root-mean-square error.

EJNMMI Research

Expert writing Tips

Best practice

When reporting the "mean square error", clearly state the units of measurement of the target variable, as the MSE is in squared units.

Common error

Don't confuse "mean square error" with root mean square error (RMSE). Remember that RMSE is simply the square root of the MSE and has the advantage of being in the same units as the original data, making it easier to interpret.

Linguistic Context

The phrase "mean square error" functions as a noun phrase that identifies a specific statistical measure. As Ludwig AI confirms, this phrase is indeed correct. It's predominantly used in scientific and technical writing to quantify the difference between predicted and observed values.

Expression frequency: Common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "mean square error" is a common and grammatically correct term, as confirmed by Ludwig AI, used primarily in scientific and technical fields to measure the difference between predicted and actual values. Its function is to quantify the accuracy of models. While alternatives like "average squared error" exist, understanding the nuances between MSE and RMSE (Root Mean Square Error) is crucial. Keep in mind that the original examples from the Ludwig sources, belong to the Science category, so usage in more informal context may sound out of place.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

average squared deviation

Replaces "square" with "squared" and "error" with "deviation", focusing on the averaging aspect.

mean squared deviation

Similar to "average squared deviation", but maintains the word "mean".

average squared error

Swaps "mean" for "average" but maintains the core concept.

expected squared error

Emphasizes the statistical expectation of the squared error.

quadratic loss

Uses a more general term for a loss function based on the square of the difference.

L2 loss

Refers to the L2 norm, which is mathematically equivalent to the square root of the mean square error.

root mean square error

Presents the square root of the mean square error, often used for interpretability.

prediction error variance

Focuses on the variance of the prediction errors.

residual variance

Highlights the variance of the residuals (the differences between observed and predicted values).

error variance

A more general term referring to the variance of the errors in a model.

FAQs

How is "mean square error" calculated?

The "mean square error" (MSE) is calculated by taking the average of the squares of the differences between the predicted values and the actual values. This provides a measure of the overall quality of a model's predictions.

What's the difference between "mean square error" and "root mean square error"?

The "mean square error" (MSE) is the average of the squared differences between predicted and actual values, while the "root mean square error" (RMSE) is the square root of the MSE. RMSE is often preferred because it's in the same units as the original data, making it easier to interpret.

When should I use "mean square error"?

Use "mean square error" when you want to quantify the overall difference between predicted and actual values, giving more weight to larger errors due to the squaring operation. It's particularly useful when comparing different models or algorithms.

What are some alternatives to "mean square error"?

Alternatives to "mean square error" include "mean absolute error" (MAE), which is less sensitive to outliers, and "root mean square error" (RMSE), which is in the same units as the original data.