Naive Bayes Classifier 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 example 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] The bayes error example 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 set
Bayes Error Rate Explained
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 rate may error rate definition 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&oldid=743880528" Categories: Statistical classific
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Error Rate Classification
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Estimating The Bayes Error Rate Through Classifier Combining
about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow classification error rate in r 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 https://en.wikipedia.org/wiki/Bayes_error_rate 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 the error http://stackoverflow.com/questions/22766910/error-during-naive-bayes-classifier rate using accuracy metric but unfortunately an error is being thrown can you please help me out.. b=as.factor(test_data$Personal.Loan) model_naivebayes = naiveBayes(Personal.Loan ~.,data=train_data); naive_predict = predict(model_naivebayes, test_data); table(naive_predict,b) Error: Error in table(naive_predict, b) : all arguments must have the same length when I checked the contents in naive_predict it say Factor W/ '0' evels Regards, Sri. r bayesian share|improve this question asked Mar 31 '14 at 16:22 Sriharsha Ramaraju 12 If you please update your question with a minimal, reproducible example it will be much easier to help you. –Roman Tsegelskyi Mar 31 '14 at 17:54 add a comment| 2 Answers 2 active oldest votes up vote 0 down vote Looks like the error is on the 3rd line. You need to exclude your class variables when predicting. naive_predict = predict(model_naivebayes, test_data[,-which(names(predictors) %in% c("Personal.Loans"))]; share|improve this answer answered May 22 '15 at 12:00 polyphant 405513 add a comment| up vote 0 down vote I had similar issue and resolved it by this way. I will show it with the iris
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta http://stackoverflow.com/questions/17322122/naive-bayes-classifier-error-in-r Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like error rate you, helping each other. Join them; it only takes a minute: Sign up naive bayes classifier error in r up vote 0 down vote favorite I use naiveBayes e1071 for classifying my data set (Classification class: "V32" 0/1). Here is what I do: d <- read.table("Modeling_Data.txt",header=FALSE,sep="\t", comment.char="",quote="") #divide into training and test data 70:30 trainingIndex <- createDataPartition(d$V32, bayes error rate p=.7, list=F) d.training <- d[trainingIndex,] d.testing <- d[-trainingIndex,] nb.classifier <- naiveBayes(as.factor(d$V32) ~ ., data = d.training) But I get this error: Error in names(dimnames(tables[[i]])) <- c(Yname, colnames(x)[i]) : attempt to set an attribute on NULL predict(nb.classifier,d.testing[,-50000]) Error in predict(nb.classifier, d.testing[, -50000]) : object 'nb.classifier' not found I tried to use the included the data set (iris) and everything works fine. What's wrong with my approach? r machine-learning classification share|improve this question edited Jun 26 '13 at 15:28 agstudy 80.3k783136 asked Jun 26 '13 at 13:56 Dennis Ananth 31 add a comment| 1 Answer 1 active oldest votes up vote 0 down vote accepted Seems like building of the model failing (and as a result the classifier is not constructed). Without looking at your data, my best guess would be that you have incomplete cases. You could try removing cases with missing data using complete.cases as follows. d <- read.table("Modeling_Data.txt",header=FALSE,sep="\t",comment.char="",quote="") # remove incomplete cases d[complete.cases(d),] # divide into training and test data 70:30 trainingIndex <- createDataPartition(d$V32, p=.7, list=F) share