Naive Bayes Classifier Error Rate
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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. bayes error rate example One method seeks to obtain analytical bounds which are inherently dependent bayes error rate in r on distribution parameters, and hence difficult to estimate. Another approach focuses on class densities, while yet another method bayes error example combines and compares various classifiers.[2] 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 error rate definition and pattern classification, the labels of a 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
Bayes Error Rate Explained
probabilities given the predictors. For a multiclass classifier, the Bayes error 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.s
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Error Rate Classification
site About Us Learn more about Stack Overflow the company Business Learn estimating the bayes error rate through classifier combining more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross classification error rate in r 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 https://en.wikipedia.org/wiki/Bayes_error_rate it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Accuracy rate in naive Bayes classification up vote 1 down vote favorite I am trying to use a naive Bayes classification technique to predict fraudsters (Caller). My training set of 138 instances has 5 columns viz. Morning, Afternoon, Evening, Night and Caller. Morning has http://stats.stackexchange.com/questions/86024/accuracy-rate-in-naive-bayes-classification 8 names; the rest all have 3. The names are unique to each column. > levels(mlcallers$Morning) [1] "Kelly" "Larry" "Mark" "Nancy" "Olga" "Peter" "Quentin" "Robert" > levels(mlcallers$Afternoon) [1] "George" "Harry" "John" > levels(mlcallers$Evening) [1] "David" "Emily" "Frank" > levels(mlcallers$Night) [1] "Alex" "Beth" "Clark" > levels(mlcallers$Caller) [1] "Sally" "Vince" "Virginia" ## Training data set > summary(mlcallers) Morning Afternoon Evening Night Caller Olga :29 George:31 David:35 Alex :43 Sally :47 Peter :29 Harry :49 Emily:44 Beth :52 Vince :43 Quentin:22 John :58 Frank:59 Clark:43 Virginia:48 Robert :13 Mark :12 Nancy :12 (Other):21 ## Test data set > summary(testdata) Morning Afternoon Evening Night Kelly :2 George:7 David:7 Alex :6 Larry :1 Harry :3 Emily:1 Beth :2 Mark :1 John :5 Frank:7 Clark:7 Nancy :1 Olga :1 Quentin:4 Robert :5 I need to predict the Caller (probable fraudster) in the test data set and also report their confidence. My attempt is as follows: > library(e1071) > model <- naiveBayes(Caller~., data=mlcallers) > predict(model, testdata) [1] Sally Sally Sally Vince Sally Vince Vince Virginia Virginia Virginia Virginia Sally [13] Sally Virginia Sally Levels: Sally Vince Virginia > predict(model, testdata, type="raw") Sally Vince Virginia [1,] 0.81806260 0.155576135 0.026361264 [
here for a quick overview of the site Help Center Detailed answers to http://stackoverflow.com/questions/9967089/misclassification-error-rate-and-accuracy any questions you might 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 error rate Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Misclassification error rate and accuracy up vote 2 down vote favorite Below is a Matlab code for Bayes classifier which classifies arbitrary numbers into their classes. training = bayes error rate [3;5;17;19;24;27;31;38;45;48;52;56;66;69;73;78;84;88]; target_class = [0;0;10;10;20;20;30;30;40;40;50;50;60;60;70;70;80;80]; test = [1:2:90]'; class = classify(test,training, target_class, 'diaglinear'); % Naive Bayes classifier [test class] (a) If someone could provide code snippets for calculating the Bayes error for misclassification and accuracy. I went through matlab's documentation regarding [class,err]=classify(...). But, I am unable to follow it and work. (b) Also, how to plot a scatter plot and histogram indicating the number of data points belonging to different classes? I tried out with scatter(training(:),target_class(:)) but it gives something else! (c) How to work with crossvalidate()? An example would really help.Thank you. classification matlab pattern-recognition share|improve this question edited Apr 2 '12 at 1:11 asked Apr 1 '12 at 18:34 Chaitali 70110 add a comment| 1 Answer 1 active oldest votes up vote 2 down vote accepted (a) To calculate misclassification error you need to know test_class as well. Then you can compare the output class variable with test_class. misserr = sum(test_cl