> am trying to implement the code of the e1071 package for naive bayes,
> but it doens't really work, any ideas??
> am very glad about any help!!
> need a naive bayes with 10-fold cross validation:
The caret package will do this. Use
fit <- train(
x, y, method = "nb",
trControl = trainControl(method = "cv", number = 10))
(there is no formula interface yet).
It will use the naïve Bayes implementation in klaR. Unless you specify
otherwise, it will train naïve Bayes models with and without using kernel
density estimation (but you can change that).
The object fit$finalModel will contain the model fit that is "cv optimal".
For example:
> fit <- train(
+ iris[,-5], iris$Species, "nb",
+ trControl = trainControl(method = "cv", number = 10))
Iter 1 Values: TRUE
Loading required package: MASS
Loading required package: class
Iter 2 Values: FALSE
>
> fit
Call:
train.default(x = iris[, -5], y = iris$Species, method = "nb",
trControl = trainControl(method = "cv", number = 10))
150 samples
4 predictors
summary of cross-validation (10 fold) sample sizes:
135, 135, 135, 135, 135, 135, ...
cv resampled training results across tuning parameters:
usekernel Accuracy Kappa Accuracy SD Kappa SD Optimal
FALSE 0.953 0.93 0.0706 0.106
TRUE 0.96 0.94 0.0562 0.0843 *
Accuracy was used to select the optimal model
Max
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of "Julia Kröpfl"
Sent: Tuesday, October 30, 2007 4:46 PM
To: r-help@r-project.org
Subject: [R] NAIVE BAYES with 10-fold cross validation
hi there!!
i am trying to implement the code of the e1071 package for naive bayes, but it
doens't really work, any ideas??
i am very glad about any help!!
i need a naive bayes with 10-fold cross validation:
code:
library(e1071)
model <- naiveBayes(code ~ ., mydata)
tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate =
min,
sampling = c("cross"), sampling.aggregate = mean,
cross = 10, best.model = TRUE, performances = TRUE)
pred <- predict(model, mydata[,-12], type="class")
tune(naiveBayes, code~., mydata, predict.fun=pred, tune.control)
thx for your help!
cheers, julia
--
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