Mean Absolute Error Formula In Weka
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The equation is given in the library references. Expressed in words, the MAE is the average over the verification sample of relative absolute error weka the absolute values of the differences between forecast and the corresponding
Root Relative Squared Error
observation. The MAE is a linear score which means that all the individual differences are weighted equally in relative absolute error meaning the average. Root mean squared error (RMSE) The RMSE is a quadratic scoring rule which measures the average magnitude of the error. The equation for the RMSE is given
Weka "mean Absolute Error"
in both of the references. Expressing the formula in words, the difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the mean absolute error interpretation RMSE is most useful when large errors are particularly undesirable. The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts. The RMSE will always be larger or equal to the MAE; the greater difference between them, the greater the variance in the individual errors in the sample. If the RMSE=MAE, then all the errors are of the same magnitude Both the MAE and RMSE can range from 0 to ∞. They are negatively-oriented scores: Lower values are better. Loading Questions ... You read that a set of temperature forecasts shows a MAE of 1.5 degrees and a RMSE of 2.5 degrees. What does this mean? Choose the best answer: Feedback This is true, but not the best answer. If RMSE>MAE, then there is variation in the errors. Feedback This is true too, the RMSE-MAE difference isn't large enough to indicate the presence of very large errors. Feedback This is true, by the definition of the MAE, but not the best answer.
Content as Inappropriate ♦ ♦ root mean squared error calculation? I'm curious about the formula used by Weka to calculate the root mean squared error. I know this has been asked on the maillist before, but, pardon
Relative Absolute Error Formula
my ignorance, I'm still confused. I would have guessed the formula was sqrt(sum((xi-yi)^2) / n)
Understanding Weka Output
where x is a vector of calculated values and y is a vector of actual values and n is the number of relative absolute error formula in weka observations. But when I use that formula to manually calculate RMSE, I get a different answer than what Weka generates. Below, Weka shows a value of .1527, but I calculate .16667. For the below example, I define http://www.eumetcal.org/resources/ukmeteocal/verification/www/english/msg/ver_cont_var/uos3/uos3_ko1.htm an output class as a nominal type with possible values of 0 and 1. Correctly Classified Instances 175 97.2222 % Incorrectly Classified Instances 5 2.7778 % Kappa statistic 0.943 Mean absolute error 0.0466 Root mean squared error 0.1527 Relative absolute error 9.627 % Root relative squared error 31.0329 % Total Number of Instances 180 === Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure Class 0.986 0.038 0.948 http://weka.8497.n7.nabble.com/root-mean-squared-error-calculation-td19651.html 0.986 0.967 0 0.962 0.014 0.99 0.962 0.976 1 === Confusion Matrix === a b <-- classified as 73 1 | a = 0 4 102 | b = 1 I've been told that "The sum is taken over all class values, as well as over all instances," so my formula is probably wrong in that it's not adding in some extra squared errors, but I don't really know what that means. The Weka Explorer User Guide page 7 says "By default, the class is taken to be the last attribute in the data." So to me that implies that by default there will be one class. Bottom line - If someone could show me the formula or, more importantly, show me explicitly how the formula is used to generate the above RMSE value of .1572, I would be forever grateful. Thanks, John _______________________________________________ Wekalist mailing list [hidden email] https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist Peter Reutemann Reply | Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: root mean squared error calculation? > I'm curious about the formula used by Weka to calculate the root mean > squared error. I know this has been asked on the maillist before, but, > pardon my ignorance, I'm s
here for a quick overview of the site Help Center Detailed answers to any questions you might http://stackoverflow.com/questions/10776673/formula-for-relative-absolute-error-and-root-relative-squared-error-used-in have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million absolute error programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Formula for “Relative absolute error” and “Root relative squared error” used in machine learning (as computed by Weka) up vote 6 down vote favorite In open source data mining software Weka (written in Java), when I run some data relative absolute error mining algorithm like Linear regression Weka returns model and some model evaluating metrics for test data. It looks like this: Correlation coefficient 0.2978 Mean absolute error 15.5995 Root mean squared error 29.9002 Relative absolute error 47.7508 % Root relative squared error 72.2651 % What is the formula for "Relative absolute error" and "Root relative squared error"? I cannot figure that out. I would like to use this metrics to evaluate my own algorithms in Matlab. machine-learning data-mining weka share|improve this question edited May 27 '12 at 22:06 Amro 102k18163302 asked May 27 '12 at 19:35 drasto 4,6262799172 add a comment| 2 Answers 2 active oldest votes up vote 6 down vote accepted From this presentation, in slide 22, and citing witten, here are the formulas: Relative absolute error Root relative squared error with Actual target values: a1 a2 … an Predicted target values: p1 p2 … pn share|improve this answer edited May 27 '12 at 21:09 answered May 27 '12 at 19:55 Christopher
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