Normalised Absolute Error
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Mean Absolute Error Interpretation
files 420 downloads 4.55925 12 Aug 2009 (Updated 07 Jun 2011) Image quality measures are calculated for a distorted image with reference to an original image NormalizedAbsoluteError(origImg, distImg) Contact us MathWorks Accelerating the pace of engineering and science MathWorks is the leading developer of mathematical computing software for engineers and scientists. Discover... Explore Products MATLAB Simulink Student Software Hardware Support File Exchange Try or Buy Downloads Trial Software Contact Sales Pricing and Licensing Learn to Use Documentation Tutorials Examples Videos and Webinars Training Get Support Installation Help Answers Consulting License Center About MathWorks Careers Company Overview Newsroom Social Mission © 1994-2016 The MathWorks, Inc. Patents Trademarks Privacy Policy Preventing Piracy Terms of Use RSS Google+ Facebook Twitter
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Mean Relative Error
Discuss the workings and policies of this site About Us Learn mean absolute percentage error more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us relative absolute error Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, https://www.mathworks.com/matlabcentral/fileexchange/25005-image-quality-measures/content/ImageQualityMeasures/NormalizedAbsoluteError.m and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Mean absolute error OR root mean squared error? up vote 25 down vote favorite 12 Why use Root Mean Squared Error (RMSE) instead http://stats.stackexchange.com/questions/48267/mean-absolute-error-or-root-mean-squared-error of Mean Absolute Error (MAE)?? Hi I've been investigating the error generated in a calculation - I initially calculated the error as a Root Mean Normalised Squared Error. Looking a little closer, I see the effects of squaring the error gives more weight to larger errors than smaller ones, skewing the error estimate towards the odd outlier. This is quite obvious in retrospect. So my question - in what instance would the Root Mean Squared Error be a more appropriate measure of error than the Mean Absolute Error? The latter seems more appropriate to me or am I missing something? To illustrate this I have attached an example below: The scatter plot shows two variables with a good correlation, the two histograms to the right chart the error between Y(observed ) and Y(predicted) using normalised RMSE (top) and MAE (bottom). There are no significant outliers in this data and MAE gives a lower error than RMSE. Is there any rational, other than MAE being preferable, for using one measure of er
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn http://math.stackexchange.com/questions/677852/how-to-calculate-relative-error-when-true-value-is-zero more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Mathematics Questions Tags Users Badges Unanswered Ask Question _ Mathematics Stack Exchange is a question and answer site for people studying math at any level and professionals in related fields. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can absolute error answer The best answers are voted up and rise to the top How to calculate relative error when true value is zero? up vote 10 down vote favorite 3 How do I calculate relative error when the true value is zero? Say I have $x_{true} = 0$ and $x_{test}$. If I define relative error as: $\text{relative error} = \frac{x_{true}-x_{test}}{x_{true}}$ Then the relative error is always undefined. If mean absolute error instead I use the definition: $\text{relative error} = \frac{x_{true}-x_{test}}{x_{test}}$ Then the relative error is always 100%. Both methods seem useless. Is there another alternative? statistics share|cite|improve this question asked Feb 15 '14 at 22:41 okj 9511818 1 you need a maximum for that.. –Seyhmus Güngören Feb 15 '14 at 23:06 1 Simple and interesting question, indeed. Could you tell in which context you face this situation ? Depending on your answer, there are possible alternatives. –Claude Leibovici Feb 16 '14 at 6:24 1 @ClaudeLeibovici: I am doing a parameter estimation problem. I know the true parameter value ($x_{true}$), and I have simulation data from which I infer an estimate of the parameter ($x_{test}$). I want to quantify the error, and it seems that for my particular case relative error is more meaningful than absolute error. –okj Feb 17 '14 at 14:05 1 What about $\text{error} = 2 \frac{x_{true}-x_{test}}{x_{true}+x_{test}}$ if it is for an a posteriori analysis ? –Claude Leibovici Feb 17 '14 at 14:16 1 @okj. I am familiar with this situation. Either use the classical relative error and return $NaN$ if $x_{true}=0$ either adopt this small thing. It is always the same pr
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