search for: besttun

...n 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 which I got :* you can see some of the predictors are missing like V4, V6, V7 V1 V2 V3 V5 V8 V9 V10 V11 V13 V14...

...s.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]<-tune_best[[1]] } -- View this message in context: http://r.789695.n4.nabble.com/Train-error-subscript-out-of-bonds-tp3234510p3234510.html Sent from the R help mailing list archive at Nabble.com.

...tree=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 > > > > ################################################# > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read th...

...utline 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 #################################################

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 +