r-caret | 易学教程

getting this error in Caret

阅读更多关于 getting this error in Caret

问题 I'm getting the following error and I don't know what may have gone wrong. I'm using R Studio with the 3.1.3 version of R for Windows 8.1 and using the Caret package for datamining. I have the following training data: str(training) 'data.frame': 212300 obs. of 21 variables: $ FL_DATE_MDD_MMDD : int 101 101 101 101 101 101 101 101 101 101 ... $ FL_DATE : int 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 1012013 ... $ UNIQUE_CARRIER : Factor w/ 13 levels "9E","AA","AS"

Error when using predict() on a randomForest object trained with caret's train() using formula

阅读更多关于 Error when using predict() on a randomForest object trained with caret's train() using formula

问题 Using R 3.2.0 with caret 6.0-41 and randomForest 4.6-10 on a 64-bit Linux machine. When trying to use the predict() method on a randomForest object trained with the train() function from the caret package using a formula, the function returns an error. When training via randomForest() and/or using x= and y= rather than a formula, it all runs smoothly. Here is a working example: library(randomForest) library(caret) data(imports85) imp85 <- imports85[, c("stroke", "price", "fuelType",

Error in ConfusionMatrix the data and reference factors must have the same number of levels

阅读更多关于 Error in ConfusionMatrix the data and reference factors must have the same number of levels

问题 I've trained a tree model with R caret. I'm now trying to generate a confusion matrix and keep getting the following error: Error in confusionMatrix.default(predictionsTree, testdata$catgeory) : the data and reference factors must have the same number of levels prob <- 0.5 #Specify class split singleSplit <- createDataPartition(modellingData2$category, p=prob, times=1, list=FALSE) cvControl <- trainControl(method="repeatedcv", number=10, repeats=5) traindata <- modellingData2[singleSplit,]

parRF on caret not working for more than one core

阅读更多关于 parRF on caret not working for more than one core

问题 parRF from the caret R package is not working for me with more than one core, which is quite ironic, given the par in parRF stands for parallel. I'm on a windows machine, if that is a relevant piece of information. I checked that I'm using the latest an greatest regarding caret and doParallel. I made a minimal example and and give the results below. Any ideas? Source code library(caret) library(doParallel) trCtrl <- trainControl( method = "repeatedcv" , number = 2 , repeats = 5 ,

Fully reproducible parallel models using caret

阅读更多关于 Fully reproducible parallel models using caret

When I run 2 random forests in caret, I get the exact same results if I set a random seed: library(caret) library(doParallel) set.seed(42) myControl <- trainControl(method='cv', index=createFolds(iris$Species)) set.seed(42) model1 <- train(Species~., iris, method='rf', trControl=myControl) set.seed(42) model2 <- train(Species~., iris, method='rf', trControl=myControl) > all.equal(predict(model1, type='prob'), predict(model2, type='prob')) [1] TRUE However, if I register a parallel back-end to speed up the modeling, I get a different result each time I run the model: cl <- makeCluster

Error when I try to predict class probabilities in R - caret

阅读更多关于 Error when I try to predict class probabilities in R - caret

问题 I've build a model using caret. When the training was completed I got the following warning: Warning message: In train.default(x, y, weights = w, ...) : At least one of the class levels are not valid R variables names; This may cause errors if class probabilities are generated because the variables names will be converted to: X0, X1 The names of the variables are: str(train) 'data.frame': 7395 obs. of 30 variables: $ alchemy_category : Factor w/ 13 levels "arts_entertainment",..: 2 8 6 6 11 6