similar to: Specify feature weights in model prediction (CARET)

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks,

Hi. I am trying to construct a svmLinear model using the "caret" package (see code below). Using the same data, without changing any setting, sometimes it constructs the model successfully, and sometimes I get an index out of bounds error. Is this unexpected behaviour? I would appreciate any insights this issue. Thanks. ~Kendric > train.y [1] S S S S R R R R R R R R R R R R R R R

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 =

Version 4.39 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 75 different models for classification and regression. See the package

Version 4.39 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 75 different models for classification and regression. See the package

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

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

I'm 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

Greetings, I have been exploring the use of the caret package to conduct some plsda modeling. Previously, I have come across methods that result in a R2 and Q2 for the model. Using the 'iris' data set, I wanted to see if I could accomplish this with the caret package. I use the following code: library(caret) data(iris) #needed to convert to numeric in order to do regression #I

H2O va bien (muy bien) tanto en un ordenador sobremesa/portátil y sobre un clúster. En uno de sobremesa si tienes buena RAM y muchos cores, mejor. Y no tienes porqué usar Spark si no necesitas una solución tiempo real o "near real-time". H2O tiene otra solución para interaccionar con Spark (Sparkling Water). Incluso sobre un clúster, puedes usar "sparklyr" y

Dear all, I 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,

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

I am currently doing some prediction work using the knn script in the 'class' package. Does anyone know a way of having R return the IDs (sample IDs, or column IDs of the training matrix) of the 'k' samples that are chosen by the algorithm as being nearest to a given test sample? I have searched/read everything I can about the script, however have not found anything other than the

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

The documentation for caret::train shows a list of parameters that one can tune for each method classification/regression method. For example, for the method randomForest one can tune mtry in the call to train. But the function call to train random forests in the original package has many other parameters, e.g. sampsize, maxnodes, etc. Is there **any** way to access these parameters using train

Hello there: I got 13 variables in my training/target set, the first 12 variables are mixture of numerical and categorical variables. The last one is the one I need to predict, and it is a numerical variable. >train<-read.table("train.txt") >test<-read.table("test.txt") >cl<-factor(train[,13]) >pred<-knn(train, test, clk=3, prob=TRUE) >pred I got

Si nos dices el tipo de problema que estás intentando solucionar y el tamaño del dataset podemos recomendarte algo más. En tu pseudo-código mezclas algoritmos supervisados y no-supervisados. Además de ranger, daría alguna oportunidad a "gbm" o como no a "xgboost". Y éstos los probaría dentro de H2O. Saludos, Carlos Ortega www.qualityexcellence.es El 4 de junio de 2017,

Folks: I keep getting the following error message (I'm on Windows 7, R-2.15.2, and tried a reboot...). Thx! John > install.packages("caret")Installing package(s) into ‘C:/Program Files/R/R-2.15.2/library’ (as ‘lib’ is unspecified)trying URL 'http://streaming.stat.iastate.edu/CRAN/bin/windows/contrib/2.15/caret_5.15-045.zip'Content type 'application/zip' length