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Cross Error Rate

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assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants cross validation error rate to estimate how accurately a predictive model will perform in practice.

Cross Over Error Rate

In a prediction problem, a model is usually given a dataset of known data on which training

Error Rate Calculation

is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset).[4] The goal of cross validation is

Error Rate Running Record

to define a dataset to "test" the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem), etc. One round of cross-validation involves partitioning a sample of error rate statistics data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. One of the main reasons for using cross-validation instead of using the conventional validation (e.g. partitioning the data set into two sets of 70% for training and 30% for test) is that there is not enough data available to partition it into separate training and test sets without losing significant modelling or testing capability. In these cases, a fair way to properly estimate model prediction performance is to use cross-validation as a powerful general technique.[5] In summary, cross-validation combines (averages) measures of fit (prediction error) to derive a more accurate estimate of model prediction performance.[5] Contents 1 Purpose of cross-validation 2 Common types of cross-validation 2.1 Exhaustive cross-validation 2.1.1 Leave-p-out cross-validation 2.1.2 Leave-one-out cross-validation 2.2 Non-exhaustive

Please note that Internet Explorer version 8.x will not be supported as of January 1, 2016. Please refer to this blog post for more error rate definition information. Close ScienceDirectSign inSign in using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or raw read error rate password?Sign in via your institutionOpenAthens loginOther institution loginHelpJournalsBooksRegisterJournalsBooksRegisterSign inHelpcloseSign in using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in equal error rate via your institutionOpenAthens loginOther institution login Purchase Help Direct export Export file RIS(for EndNote, Reference Manager, ProCite) BibTeX Text RefWorks Direct Export Content Citation Only https://en.wikipedia.org/wiki/Cross-validation_(statistics) Citation and Abstract Advanced search JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. This page uses JavaScript to progressively load the article content as a user scrolls. Click the View full text link http://www.sciencedirect.com/science/article/pii/S0167947309001601 to bypass dynamically loaded article content. View full text Computational Statistics & Data AnalysisVolume 53, Issue 11, 1 September 2009, Pages 3735–3745 Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrapJi-Hyun Kim, Department of Statistics and Actuarial Science, Soongsil University, Dongjak-Ku Sangdo-Dong, Seoul 156-743, Republic of KoreaReceived 26 September 2006, Revised 17 April 2008, Accepted 21 April 2009, Available online 4 May 2009AbstractWe consider the accuracy estimation of a classifier constructed on a given training sample. The naive resubstitution estimate is known to have a downward bias problem. The traditional approach to tackling this bias problem is cross-validation. The bootstrap is another way to bring down the high variability of cross-validation. But a direct comparison of the two estimators, cross-validation and bootstrap, is not fair because the latter estimator requires much heavier computation. We performed an empirical study to compare the.632+bootstrap estimator with the repeated 10-fold cross-validation and the repeated one-third holdout estimator. All the estimators were set to require about the same amount of computation. In the simulation study, the repeated 10-fold cross-validation estimator was found

Tour Start 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 http://stats.stackexchange.com/questions/133458/is-accuracy-1-test-error-rate this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross https://www.jstor.org/stable/2288636 Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how error rate it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Is accuracy = 1- test error rate up vote 6 down vote favorite Apologies if this is a very obvious question, but I have been reading various posts and can't seem to find a good confirmation. In the case of classification, is a cross error rate classifier's accuracy = 1- test error rate? I get that accuracy is TP+TN/P+N, but my question is how exactly are accuracy and test error rate related. classification terminology share|improve this question edited Jan 15 '15 at 0:03 mbq 17.7k849103 asked Jan 14 '15 at 21:47 micro_gnomics 313 add a comment| 1 Answer 1 active oldest votes up vote 4 down vote In principle yes, accuracy is the fraction of properly predicted cases thus 1-the fraction of misclassified cases, that is error (rate). Both terms may be sometimes used in a more vague way, however, and cover different things like class-balanced error/accuracy or even F-score or AUROC -- it is always best to look for/include a proper clarification in the paper or report. Also note that test error rate implies error on a test set, so it is likely 1-test set accuracy, and there may be other accuracies flying around. share|improve this answer answered Jan 15 '15 at 0:03 mbq 17.7k849103 Yeah, I think that is the issue I was having is that the terms are used vaguely, and you make a good point that it must be reported in t

Login Help Contact Us About Access You are not currently logged in. Access your personal account or get JSTOR access through your library or other institution: login Log in to your personal account or through your institution. Journal of the American Statistical Asso... Vol. 78, No. 382, Jun., 1983 Estimating the Error... Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation Bradley Efron Journal of the American Statistical Association Vol. 78, No. 382 (Jun., 1983), pp. 316-331 Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association DOI: 10.2307/2288636 Stable URL: http://www.jstor.org/stable/2288636 Page Count: 16 Download ($14.00) Cite this Item Cite This Item Copy Citation Export Citation Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text file (For BibTex) Note: Always review your references and make any necessary corrections before using. Pay attention to names, capitalization, and dates. × Close Overlay Journal Info Journal of the American Statistical Association Description: The Journal of the American Statistical Association (JASA) has long been considered the premier journal of statistical science. Science Citation Index reported JASA was the most highly cited journal in the mathematical sciences in 1991-2001, with 16,457 citations, more than 50% more than the next most highly cited journals. Articles in JASA focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences and on new methods of statistical education. Coverage: 1922-2010 (Vol. 18, No. 137 - Vol. 105, No. 492) Moving Wall Moving Wall: 5 years (What is the moving wall?) Moving Wall The "moving wall" represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. Moving walls are generally represented in years. In rare instances, a publisher has elected to have a "zero" moving wall, so their current issues are available in JSTOR shortly after publication. Note: In calculating the moving wall, the current year is not counted. For example, if the current year is 2008 and a journal has a 5 year moving wall, articles from the year 2002 are available. Terms Related to the Moving Wall Fixed walls: Journals with no new volumes being added to

 

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