search for: tunegrid

...> > > customRF$levels <- function(x) x$classes > > > > > > # Set grid search parameters > > control <- trainControl(method="repeatedcv", number=10, repeats=3, > search='grid') > > > > # Outline the grid of parameters > > tunegrid <- expand.grid(.maxnodes=c(10,20,30,50), .ntree=c(100, 200, > 300)) > > set.seed(seed) > > > > # Train the model > > rf_gridsearch <- train(x=X_train_, y=y_train_, method=customRF, > metric=metric, tuneGrid=tunegrid, trControl=control) > > > > plot(rf...

...5772 16 8.3776 5.0882 17 8.6567 7.2640 18 20.9386 20.1107 19 12.2903 4.7864 20 10.5920 7.5204 21 10.2679 9.5493 22 6.2023 11.2333 23 -5.0720 -4.8701 24 6.6417 11.5139 > svmLinearGrid <- expand.grid(.C=0.1) > svmLinearFit <- train(train.x, train.y, method="svmLinear", tuneGrid=svmLinearGrid) Fitting: C=0.1 Error in indexes[[j]] : subscript out of bounds > svmLinearFit <- train(train.x, train.y, method="svmLinear", tuneGrid=svmLinearGrid) Fitting: C=0.1 maximum number of iterations reached 0.0005031579 0.0005026807maximum number of iterations reached 0.00...

...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 = expand.grid(.sigma=c(0.0118),.C=c(8,16,32,64,128)), trControl=MyTrainControl, fit = FALSE ) Once this returns my ideal parameters, in this case Cost of 64, do I simply just re-run the whole process again, passing a grid only containing the specific parameters? lik...

...anual and the vignettes but unfortunately I´m getting an error message I can`t figure out. Here is my code: rfControl <- trainControl(method = "oob", returnResamp = "all", returnData=TRUE, verboseIter = TRUE) rftrain <- train(x=train_x, y=trainclass, method="rf", tuneGrid=tuneGrid, tr.control=rfControl) pred <- predict(rftrain) pred # this works fine expred <- extractPrediction(rftrain) Error in models[[1]]$trainingData : $ operator is invalid for atomic vectors My predictors are 28 numeric attributes and one factor. I`m working with the latest version...

...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 actual arguments The same error occured wit...

...newdata, type = "prob") customRF$sort <- function(x) x[order(x[,1]),] customRF$levels <- function(x) x$classes ? # Set grid search parameters control <- trainControl(method="repeatedcv", number=10, repeats=3, search='grid') ? # Outline the grid of parameters tunegrid <- expand.grid(.maxnodes=c(10,20,30,50), .ntree=c(100, 200, 300)) set.seed(seed) ? # Train the model rf_gridsearch <- train(x=X_train_, y=y_train_, method=customRF, metric=metric, tuneGrid=tunegrid, trControl=control) ? plot(rf_gridsearch) rf_gridsearch$bestTune ############################...

...algo alta y que la falta de ajuste es significativa, decidí probar con una red neural artificial, usando los paquetes "caret" y "nnet". He probado las siguientes opciones: NN.1 <- train(R ~ V1 + V2, data = DATOS.Codificados, method = "nnet", maxit = 10000, tuneGrid = SIZE.DECAY, trace = F, linout = TRUE) donde R² = 0.9936, y AAD = 4.87 %, pero la forma de la superficie de respuesta es muy irregular, nada parecida al modelo obtenido con "rsm". NN.2 <- train(R ~ V1 + V2 + V1:V2, data = DATOS.Codificados, method = "nnet", maxit = 1...

Dave Langer in this video https://www.youtube.com/watch?v=z8PRU46I3NY uses the titanic data as an example of using caret to create xgbTree models. The caret train() function has a tuneGrid parameter which takes a list set up like so: tune.grid <- expand.grid(eta = c(0.05, 0.075, 0.1), nrounds = c(50, 75, 100), max_depth = 6:8, min_child_weight = c(2, 2.25, 2.5), colsample_bytree =...

Dear all, I am using the "caret" Package for predictors selection with a randomForest model. The following is the train function: rfFit<- train(x=trainRatios, y=trainClass, method="rf", importance = TRUE, do.trace = 100, keep.inbag = TRUE, tuneGrid = grid, trControl=bootControl, scale = TRUE, metric = "ROC") I wanted to use ROC as the metric for variable selection. I know that this works with the logit model by making sure that classProbs = TRUE and summaryFunction = twoClassSummary in the trainControl function. However if I do th...

...with it? > rfGrid<-expand.grid(.mtry=c(1:2)) > rfectrl<-rfeControl(functions=caretFuncs,method="cv",verbose=F,returnResamp="final",number=10) > subsets<-c(3,4) > set.seed(2) > rf.RFE<-rfe(trx,try,sizes=subsets,rfeControl=rfectrl,method="rf",tuneGrid=rfGrid) Loading required package: class Attaching package: 'class' The following object(s) are masked from package:reshape : condense Fitting: mtry=1 Fitting: mtry=2 Error in varImp.randomForest(object$finalModel, ...) : subscript out of bounds In addition: Warni...

...licate(nR, rnorm(nCol))) trY <- runif(1)*trX[,1]*trX[,2]^2+runif(1)*trX[,3]/trX[,4] trY <- as.factor(ifelse(sign(trY)>0,'X1','X0')) my.grid <- createGrid(method.name, grid.len, data=trX) my.model <- train(trX,trY,method=method.name,trace=FALSE,trControl=myCtrl,tuneGrid=my.grid, metric="ROC") print("Done") The error I get is: task 2 failed - "arguments imply differing number of rows: 1334, 666" However, everything works if I reduce nR to, say 20. Any thoughts on what may be causing this? Is there a place where I could report this...

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

...8994??? 1980-04-06????????? 8.3???????????12.6 90342??? 1980-07-13????????? 18.9??????????22.3 90342??? 1980-07-14????????? 19.3??????????28.4 EXAMPLE SCRIPT FOR MODEL FITTING fitControl <- trainControl(method = "repeatedcv", number=10, repeats=3) tuning <- read.table("temptunegrid.txt",head=T,sep=",") tuning # # Model with 100 iterations registerDoMC(4) tempmod100its <- train(watmntemp~tempa + tempb + tempc + tempd + tempe + netarea + netbuffor + strmslope + netsoilprm + netslope + gwndx + mnaspect + urb + ag + forest + buffor + tempa7day + tempb7day +...

...aggregating and visualization resampling results (resamples) has been enhanced with more visualization methods. The class can also work with caret's feature selection routines (rfe() and sbf()) - the print method for train() has been improved - functions can be now be passed to the tuneGrid argument in train() - an existing function that catalogs the existing models available within train(), called modelLookup(), is now available to the users - when parallel processing, more computations are being executed in the worker processes than previously (e.g. performance calcs...

...aggregating and visualization resampling results (resamples) has been enhanced with more visualization methods. The class can also work with caret's feature selection routines (rfe() and sbf()) - the print method for train() has been improved - functions can be now be passed to the tuneGrid argument in train() - an existing function that catalogs the existing models available within train(), called modelLookup(), is now available to the users - when parallel processing, more computations are being executed in the worker processes than previously (e.g. performance calcs...

Like earth can be trained simultaneously for degree and nprune, is there a way to train ctree simultaneously for mincriterion and maxdepth? Also, I notice there are separate methods ctree and ctree2, and if both options are attempted to tune with one method, the summary averages the option it doesn't support. The full log is attached, and notice these lines below for

...col=c("red"), lty=1) #Cross validation using 10 fold CV: ctrl <- trainControl(method = "cv", classProbs = TRUE, summaryFunction = twoClassSummary) set.seed(1) rfEstimate <- train(factor(Species) ~ .,data = iris, method = "rf", metric = "ROC", tuneGrid = data.frame(.mtry = 2), trControl = ctrl) rfEstimate How can i plot the results from the cross validation on the previous ROC plot ? thanks, david

Usually when using raw glmnet I let the implementation choose the lambdas. However when training via caret::train the lambda values are predetermined. Is there any way to have caret defer the lambda choices to caret::train and thus choose the optimal lambda dynamically? -- Yang Zhang http://yz.mit.edu/

...utcome) predictions <- rep(-1,length(cv_index)) pamGrid <- seq(0.1,5,by=0.2) pamGrid <- data.frame(.threshold=pamGrid) # manual leave-one-out for (holdout in cv_index) { pamFit1 <- train(colon.x[-holdout,], outcome[-holdout], method = "pam", tuneGrid= pamGrid, trControl = trainControl(method = "cv")) predictions[holdout] = predict(pamFit1,newdata = colon.x[holdout,,drop=FALSE]) } # end example > sessionInfo() R version 2.14.2 (2012-02-29) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] LC_CTYPE...

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or