search for: traincontrol

I have carefully read the CARET documentation at: http://caret.r-forge.r-project.org/training.html, the vignettes, and everything is quite clear (the examples on the website help a lot!), but I am still a confused about the relationship between two arguments to trainControl: "method" "index" and the interplay between trainControl and the data splitting functions in caret (e.g. createDataPartition, createResample, createFolds and createMultiFolds) To better frame my questions, let me use the following example from the documentation: ***********...

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data = trainset, method="svmRadial", tuneGrid = ex...

...2.88. I use the caret package to compare different models on a dataset. In order to compare their different performances I would like to use the same data partitions for every models. I understand that using a LGOCV or a boot type re-sampling method along with the "index" argument of the trainControl function, one is able to supply a training partition to the train function. However, I would like to apply a 10-fold cross validation to validate the models and I did not find any way to supply some predefined partition (created with createFolds) in this setting. Any help ? Thank you and great pa...

...e I: Throws warning --- library(caret) data(trees) formula=Volume~Girth+Height train(formula, data=trees, method='rpart') --- Simlified Testcase II: Every other CV-method also throws the warning, for example using 'cv': --- library(caret) data(trees) formula=Volume~Girth+Height tc=trainControl(method='cv') train(formula, data=trees, method='rpart', trControl=tc) --- Simlified Testcase III: The only CV-method which is working is 'LOOCV': --- library(caret) data(trees) formula=Volume~Girth+Height tc=trainControl(method='LOOCV') train(formula, data=trees,...

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-bag (oob) error estimate from the model instead of resampling, which (in theory) is largely

...king with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add " classProbs = TRUE," in the trainControl. Thereafter everytime I run train I get this message: "undefined columns selected" I copy the syntax: fitControl <- trainControl(method = "cv", number = 10, classProbs = TRUE,returnResamp = "all", verboseIter = FALSE) glmFit <- train(Descr, Categ, method = &quot...

I want to use the caret package and found out about the timingSamps obtion to obtain the time which is needed to predict results. But, as soon as I set a value for this option, the whole model generation fails. Check this example: ------------------------- library(caret) tc=trainControl(method='LGOCV', timingSamps=10) tcWithout=trainControl(method='LGOCV') x=train(Volume~Girth+Height, method="lm", data=trees, trControl=tcWithout) x=train(Volume~Girth+Height, method="lm", data=trees, trControl=tc) Error in eval(expr, envir, enclos) : object ...

...on, I used the glmnet to model and trying to get the coefficients and the model parameters I am trying to use the extractPrediction to obtain a confusion matrix and it seems to be giving me errors. x<-read.csv("x.csv", header=TRUE); y<-read.csv("y.csv", header=TRUE); tc=trainControl(method="cv", number=10 ); glmmat<-train(x,y,method="glmnet", trControl=tc); extractPrediction(list(glmmat,testX=x,testY = y)); any help would be great thanks vss [[alternative HTML version deleted]]

Hi list, I´m working on a predictive modeling task using the caret package. I found the best model parameters using the train() and trainControl() command. Now I want to evaluate my model and make predictions on a test dataset. I tried to follow the instructions in the manual and the vignettes but unfortunately I´m getting an error message I can`t figure out. Here is my code: rfControl <- trainControl(method = "oob", returnResa...

...) function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=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...

...rain1<-trainset[,-ncol(trainset)] train1<-train1[,-(1)] test_t<-testset[,-ncol(testset)] species_test<-as.factor(testset[,ncol(testset)]) test_t<-test_t[,-(1)] #### #CARET::TRAIN #### fit1<-train(train1,as.factor(trainset[,ncol(trainset)]),"svmpoly",trControl = trainControl((method = "cv"),10,verboseIter = F),tuneLength=3) pred<-predict(fit1,test_t) t_train[[i]]<-table(predicted=pred,observed=testset[,ncol(testset)]) tune_result[[i]]<-fit1$results; tune_best<-fit1$bestTune; scale1[i]<-tune_best[[3]] degree[i]<-tune_best[[2]] c1[i]<...

...dealing with classification problems, and I'm trying to specify a custom scoring metric (recall at p, ROC, etc.) that depends on not just the class output but the probability estimates, so that caret::train can choose the optimal tuning parameters based on this metric. However, when I supply a trainControl summaryFunction, the data given to it contains only class predictions, so the only metrics possible are things like accuracy, kappa, etc. Is there any way to do this that I'm looking? If not, could I put this in as a feature request? Thanks! -- Yang Zhang http://yz.mit.edu/

Hello! I have a question. I am working with createFolds: folds<- trainControl(method='cv', index=createFolds(data$Score,list = TRUE)) I need to iterate over folds to extract the indexes from each fold. For example, if I do folds$index$Fold01, it contains: 5 11 17 29 44 50 52 64 65 I need to iterate over each $Fold_i to extract the indexes, but I can't d...

...to do regression #I don't fully understand this but if I left as a factor I would get an error following the summary function iris$Species=as.numeric(iris$Species) inTrain1=createDataPartition(y=iris$Species, p=.75, list=FALSE) training1=iris[inTrain1,] testing1=iris[-inTrain1,] ctrl1=trainControl(method="cv", number=10) plsFit2=train(Species~., data=training1, method="pls", trControl=ctrl1, metric="Rsquared", preProc=c("scale")) data(iris) training1=iris[inTrain1,] datvars=training1[,1:4] dat.sc=scale(datvars) n=nrow(dat.sc)...

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"),

Gracias Carlos. No uso caret, pero lo miraré. Quoting Carlos Ortega <cof en qualityexcellence.es>: > Hola, > > Creo que si utilizas "caret" y en la función "trainControl()" defines "oob" > como criterio de randomización, puedes luego recuperar del objeto del > modelo, las predicciones individuales... > > Saludos, > Carlos Ortega > www.qualityexcellence.es > > > > 2018-05-31 12:56 GMT+02:00 Manuel Mendoza <mmendoza en...

...lling to help me? Thanks, Robin http://www.epa.gov/wed/pages/models/rivpacs/rivpacs.htm > TrainDataDFAgrps2 <-predcal > TrainClassesDFAgrps2 <-grp.2; > DFAgrps2Fit1 <- train(TrainDataDFAgrps2, TrainClassesDFAgrps2, + method = "lda", + tuneLength = 10, + trControl = trainControl(method = "cv")); Error in train.default(TrainDataDFAgrps2, TrainClassesDFAgrps2, method = "lda", : wrong model type for regression > RFgrps2Fit1 <- train(TrainDataRFgrps2, TrainClassesRFgrps2, + method = "rf", + tuneLength = 10, + trControl = trainControl(me...

...t; cY<-factor(mnY); > d <- data.frame (cbind(mnX,cY)); > m<-NaiveBayes(cY~mnX, data=d); > predict(m); Error in as.vector(x, mode) : invalid argument 'mode' > library(caret); Loading required package: lattice > mCaret<-train(mnX,cY,method="nb",trControl = trainControl(method = "cv", number = 10)); Loading required package: class Fitting: usekernel=TRUE Fitting: usekernel=FALSE > predicted <- predict(mCaret, newdata=mnX); Error in 1:nrow(newdata) : NA/NaN argument > We use caret to call NaiveBayes and we don't have any error messages in ca...

...y much! ### To replicate: require(repmis) # For downloading from https df <- source_data('https://dl.dropboxusercontent.com/u/47973221/data.csv', sep=',') require(caret) svm.m1 <- train(df[,-1],df[,1],method='svmRadial',metric='Kappa',tunelength=5,trControl=trainControl(method='repeatedcv', number=10, repeats=10, classProbs=TRUE)) svm.m1 sessionInfo() ### Results - R2.15.2 > svm.m1 1241 samples 7 predictors 10 classes: ?O27479?, ?O31403?, ?O32057?, ?O32059?, ?O32060?, ?O32078?, ?O32089?, ?O32663?, ?O32668?, ?O32676? No pre-processing Resampling...

...have used following code but everytime I encounter a problem of not having coefficients for all the variables in the predictor set. # code rm(list=ls()) library(caret) # generating response and design matrix X<-matrix(rnorm(50*100),nrow=50) y<-rnorm(50*1) # Applying caret package con<-trainControl(method="cv",number=10) data<-NULL data<- train(X,y, "lasso", metric="RMSE",tuneLength = 10, trControl = con) coefs<-predict(data$finalModel,s=data$bestTune$.fraction, type ="coefficients", mode ="fraction")$coef coefs *This is the output...