Naive Bayes Error
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categories) and is analogous to the irreducible error.[1][2] A number of approaches to the estimation of the Bayes error rate exist. One method seeks to bayes error rate obtain analytical bounds which are inherently dependent on distribution parameters, and hence
Bayes Error Rate In R
difficult to estimate. Another approach focuses on class densities, while yet another method combines and compares various classifiers.[2]
Bayes Error Example
The Bayes error rate finds important use in the study of patterns and machine learning techniques.[3] Error determination[edit] In terms of machine learning and pattern classification, the labels of a
Error Rate Definition
set of random observations can be divided into 2 or more classes. Each observation is called an instance and the class it belongs to is the label. The Bayes error rate of the data distribution is the probability an instance is misclassified by a classifier that knows the true class probabilities given the predictors. For a multiclass classifier, the Bayes error error rate classification rate may be calculated as follows:[citation needed] p = ∫ x ∈ H i ∑ C i ≠ C max,x P ( C i | x ) p ( x ) d x {\displaystyle p=\textstyle \int \limits _{x\in H_{i}}\sum _{C_{i}\neq C_{\text{max,x}}}P(C_{i}|x)p(x)\,dx} where x is an instance, Ci is a class into which an instance is classified, Hi is the area/region that a classifier function h classifies as Ci.[clarification needed] The Bayes error is non-zero if the classification labels are not deterministic, i.e., there is a non-zero probability of a given instance belonging to more than one class.[citation needed] See also[edit] Naive Bayes classifier References[edit] ^ Fukunaga, Keinosuke (1990) Introduction to Statistical Pattern Recognition by ISBN 0122698517 pages 3 and 97 ^ a b K. Tumer, K. (1996) "Estimating the Bayes error rate through classifier combining" in Proceedings of the 13th International Conference on Pattern Recognition, Volume 2, 695–699 ^ Hastie, Trevor. The Elements of Statistical Learning (2nd ed.). http://statweb.stanford.edu/~tibs/ElemStatLearn/: Springer. p.17. ISBN978-0387848570. This statistics-related article is a stub. You can help Wikipedia by expanding it. v t e Retrieved from "https://en.wikipedia.org/w/index.php?title=Bayes_error_rate
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta bayes error rate explained Discuss the workings and policies of this site About Us Learn more naive bayes classifier error rate about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack estimating the bayes error rate through classifier combining 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 programmers, just like you, https://en.wikipedia.org/wiki/Bayes_error_rate helping each other. Join them; it only takes a minute: Sign up Error during naive bayes classifier up vote 1 down vote favorite I have a dataset of 5000 points and 12 attributes(out of which is class variable)..I divided data in training(3000 points) and testing(2000 points) and the performed the classification on training data and wnat to check http://stackoverflow.com/questions/22766910/error-during-naive-bayes-classifier the error rate using accuracy metric but unfortunately an error is being thrown can you please help me out.. b=as.factor(test_data$Personal.Loan) model_naivebayes = naiveBayes(Personal.Loan ~.,data=train_data); naive_predict = predict(model_naivebayes, test_data); table(naive_predict,b) Error: Error in table(naive_predict, b) : all arguments must have the same length when I checked the contents in naive_predict it say Factor W/ '0' evels Regards, Sri. r bayesian share|improve this question asked Mar 31 '14 at 16:22 Sriharsha Ramaraju 12 If you please update your question with a minimal, reproducible example it will be much easier to help you. –Roman Tsegelskyi Mar 31 '14 at 17:54 add a comment| 2 Answers 2 active oldest votes up vote 0 down vote Looks like the error is on the 3rd line. You need to exclude your class variables when predicting. naive_predict = predict(model_naivebayes, test_data[,-which(names(predictors) %in% c("Personal.Loans"))]; share|improve this answer answered May 22 '15 at 12:00 polyphant 405513 add a comment| up vote 0 down vote I had similar issue and resolved it by this way. I will show
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies http://stackoverflow.com/questions/19129141/naive-bayes-and-logistic-regression-error-rate 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 programmers, just like you, helping each other. Join them; it only takes a error rate minute: Sign up Naive Bayes and Logistic Regression Error Rate up vote 3 down vote favorite 2 I have been trying to figure out the correlation between the error rate and the number of features in both of these models. I watched some videos, and the creator of the video said that a simple model can be better than a complicated model. bayes error rate So I figured that the more features I had the greater the error rate would be. This did not prove to be true in my work, and when I had less features the error rate went up. I'm not sure if I'm doing this incorrectly, or if the guy in the video made a mistake. Can someone care to explain? I also am curious how features relate to Logistic Regression's error rate as well. machine-learning share|improve this question asked Oct 2 '13 at 2:34 Taztingo 5351520 2 This isn't a programming question; stats.stackexchange.com is more appropriate. –Dougal Oct 2 '13 at 2:43 1 That said, "a simple model can be better than a complicated model" doesn't mean a simple model is always better than a complicated model; there's a tradeoff. Otherwise a constant predictor would be the best possible model and there would be no such field as machine learning. –Dougal Oct 2 '13 at 2:44 Thank you, I will ask my questions there from now on. –Taztingo Oct 2 '13 at 3:12 The complexity of a
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