search for: tunevalu

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2008 Sep 18
1
caret package: arguments passed to the classification or regression routine
...istribution=list(name="quantile",alpha=0.5), verbose=FALSE, trControl=trainControl(method="cv",number=5), tuneGrid=gbmGrid ) Model 1: interaction.depth=1, shrinkage=0.1, n.trees=300 collapsing over other values of n.trees Error in gbm.fit(trainX, modY, interaction.depth = tuneValue$.interaction.depth, : formal argument "distribution" matched by multiple actual arguments The same error occured with distribution="laplace". I also tried the following without and success : gbm.test <- train(x.enet, y.matrix[,7], method="gbm", list(dis...
2011 Jan 24
5
Train error:: subscript out of bonds
Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8
2009 May 04
1
Caret package: coeffcients for regression
Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]
2013 Nov 15
1
Inconsistent results between caret+kernlab versions
I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +
2009 May 05
1
self organizing map advice for categorical data
...ameters. You can use coef() or any other appropriate function on this object. In the case of the elastic net, see ?predict.enet, specifically the type argument to get the regression coefficients. If you need to get to the final tuning parameters for the model, you can access them via x$finalModel$tuneValue. There are a few exceptions to this: models using S4 classes won't allow adding new objects. For SVMs, you haven't said what kernel type you used. caret uses kernel methods from kernlab (for no other reason that I like the package). The only package that I know gives the *regression* coef...