Talk:Root mean square deviation

2007 unnamed discussion

"Regression ratio"/"learning ratio"? When measuring the performance of a predictive model (e.g. regression) you can divide the RMSD by the standard deviation of the target data. When greater than or equal to one you have no learning better than the simple guessing of the mean of the data. When less than one, the prediction is more informative than just the mean. My question is: what is this measure commonly called? Uncoolbob 21:05, 15 January 2007 (UTC)

Update: colleagues are suggesting "Normalized RMSE" (or "Normalised RMSD"). Is this in wide enough use to add to the article? Uncoolbob 16:15, 16 January 2007 (UTC)

It is certainly wide enough in my field (Bioinformatics) to be useful and pertinent. The is a lot more to the story of "RMSD" than we have in this article. It could also use a lot more math theory. I also have a programme I wrote in C for calculating RMSD (it is impressively fast) and will upload the code to this site soon.--Thorwald 01:52, 17 January 2007 (UTC)

How does one interpret the RMSE value? If some results from my research yield a RMSE of, say 0.01 when comparing to an idealised estimator, what does that tell me? How can one tell if the RMSE is high or low? I think this information would be useful in this article. --Utsutsu (talk) 01:29, 3 February 2010 (UTC)

• See Mean squared error#Interpretation. Yet another reason these articles should be merged. -- DanielPenfield (talk) 03:16, 3 February 2010 (UTC)

What are the x_min and x_max to be used for the normalized RMSE computation? Should it be the range of the first variable, the second or both? -- Danielgenin (talk) 17:22, 28 December 2010 (UTC)

This is not a good article. The words are unnecessarily wordy - oblong for example. There is also unnecessary complexity such as the use of vectors to help explain the concept. The article should be simplified. — Preceding unsigned comment added by 142.52.81.11 (talk) 05:10, 6 September 2012 (UTC)

Dr. Giles's comment on this article

Dr. Giles has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:

"For an unbiased estimator, the RMSD is the square root of the variance, known as the standard error." Comment: 'standard error' should be replaced by 'standard deviation'. The term 'standard error' is universally used to refer to an estimated standard deviation, not the standard deviation itself.

We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.

Dr. Giles has published scholarly research which seems to be relevant to this Wikipedia article:

• Reference : David E. Giles, 2012. "A Note on Improved Estimation for the Topp-Leone Distribution," Econometrics Working Papers 1203, Department of Economics, University of Victoria.

ExpertIdeasBot (talk) 16:59, 19 May 2016 (UTC)

 Done +mt 21:05, 19 May 2016 (UTC)

Strange as it may seem, variance is unbiased, but standard deviation is small number biased---see https://stats.stackexchange.com/questions/249688/why-are-we-using-a-biased-and-misleading-standard-deviation-formula-for-sigma for a discussion of this. I would suggest changing the sentence to read "RMSD-squared is the variance, which is unbiased. RMSD itself, A.K.A. standard deviation, has small number bias https://stats.stackexchange.com/a/27984/99274CarlWesolowski (talk) 20:14, 24 March 2020 (UTC)

Both the original wording and Gile's were wrong, insofar as the RMSD is calculated from a number of observations of a random variable while the standard deviation is a property of the distribution. I made a correction, but am open to discussion. -St.nerol (talk) 09:11, 25 September 2023 (UTC)

I reverted my correction. It seems that the article is really about two different but closely related measures. I have not solved the problem and sorted this out completely, but just tried to clarify the ambiguity. –St.nerol (talk) 15:26, 26 September 2023 (UTC)

Standard deviation is the right word here, but I also don't think that Dr. Giles's statement is correct. Standard error refers to the standard deviation of an estimator, not an estimated standard deviation. The common usage is when estimating a mean from a sample. You calculate the sample standard deviation, sigma. Then the standard error of the sample mean, which is an estimator of the population mean, is sqrt(sigma^2 / n), where n is the sample size.

Both of those are estimates of a standard deviation. In neither case is the true standard deviation known. What makes one a standard error is that it is the SD of an estimator. The first SD tells you something inherent about the population. The second tells you about confidence in your estimate of the mean. In particular, a key difference is that the SD of the sample approaches the true population SD as n increase whereas the SE approaches 0 as n increases.

https://stats.stackexchange.com/questions/32318/difference-between-standard-error-and-standard-deviation 128.174.75.191 (talk) 21:54, 21 December 2023 (UTC)

Observed minus predicted versus predicted minus observed

The formula in this article defined residuals as the predicted value minus the observed value. I am not an expert and have been looking for verification of that definition. Different articles define residuals as observed minus predicted. There seems to be consensus that the latter definition is 'correct', or at least, accepted in a scientific context. Here is a great discussion: https://stats.stackexchange.com/questions/342466/are-residuals-predicted-minus-actual-or-actual-minus-predicted

Perhaps this article could include both definitions, and explain how each is used? 130.195.253.47 (talk) 00:45, 14 September 2023 (UTC)

In the context of this article the difference is squared, so it does not matter which way you subtract. Retimuko (talk) 02:52, 14 September 2023 (UTC)

Hi Retimuko, don't you think it matters in terms of developing understanding? Statistical terms, what statistics represent, and how different statistics relate to each other? 130.195.253.61 (talk) 02:15, 15 September 2023 (UTC)

RMSE for sample or for estimator

It seems that there is an ambiguity on whether the article is about (1) the RMSE of an estimator, which is an expected value and thus a fixed number or (2) the RMSE of a particular sample (which is a random number in the sense that it depends upon the actual sample obtained). I made light edits to the lede and the structure to clarify this ambiguity, but believe that the issue should be checked more thoroughly.

I looked in Rice (1995), and he defines MSE in the former sense, while he appears to say nothing about the latter sense. However, except for one formula, this article presently appears to be focused on the latter concept. –St.nerol (talk) 15:34, 26 September 2023 (UTC)

Are the terms in the regression equation correct?

Errors and residuals are observed values minus predicted ones. In the equation for regression RMSD it looks like they're swapped. Am I reading it incorrectly? For this particular equation, it doesn't matter the order because it's squared, but an error is what an error is, and it should be written correctly, because they don't always get squared. 128.174.75.191 (talk) 21:57, 21 December 2023 (UTC)

Requested move 26 April 2024

The following is a closed discussion of a requested move. Please do not modify it. Subsequent comments should be made in a new section on the talk page. Editors desiring to contest the closing decision should consider a move review after discussing it on the closer's talk page. No further edits should be made to this discussion.

The result of the move request was: moved. (non-admin closure) Safari Scribe^{Edits! Talk!} 15:51, 3 May 2024 (UTC)

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Root-mean-square deviation → Root mean square deviation – The hyphenation in the title appears non-standard. Of the accessible citations, all seem to use no hyphenation. Engineerchange (talk) 16:59, 26 April 2024 (UTC)

Note that root mean square also doesn't have hyphens. --Engineerchange (talk) 17:09, 26 April 2024 (UTC)

Support. (partly copied from this discussion) Technically the hyphenated version is more correct in terms of punctuation, but it seems pretty clear by Google ngrams that that spelling is falling out of favour (see here). Though, I do note that even a cursory Google search and search via Google Scholar shows the hyphenated spelling is still used often enough. ― Synpath 18:02, 27 April 2024 (UTC)

The discussion above is closed. Please do not modify it. Subsequent comments should be made on the appropriate discussion page. No further edits should be made to this discussion.