插入号训练方法抱怨有些地方不对劲；缺少所有RMSE指标值 [英] Caret train method complains Something is wrong; all the RMSE metric values are missing

问题描述

在很多情况下，尝试拟合gbm或rpart模型时，都会出现此错误.最终，我能够使用公开可用的数据一致地重现它.我注意到使用CV(或重复的Cv)时会发生此错误.当我不使用任何健身控件时，我不会收到此错误.有人能阐明为什么我总是不断出错的情况吗?

On numerous occasions I've been getting this error when trying to fit a gbm or rpart model. Finally I was able to reproduce it consistently using publicly available data. I have noticed that this error happens when using CV (or repeated cv). When I don't use any fit control I don't get this error. Can some shed some light one why I keep getting error consistently.

fitControl= trainControl("repeatedcv", repeats=5)
ds = read.csv("http://www.math.smith.edu/r/data/help.csv")
ds$sub = as.factor(ds$substance)
rpartFit1 <- train(homeless ~ female + i1 + sub + sexrisk + mcs + pcs, 
                   tcControl=fitControl, 
                   method = "rpart", 
                   data=ds)

推荐答案

有一个错字，应该是trControl而不是tcControl.并且当以tcControl形式提供参数时，caret将此参数传递给rpart并抛出错误，因为该选项从不可用.

There is a typo, it should be trControl instead of tcControl. And when the argument is provided as tcControl, caret passes this to rpart and this throws an error because this option was never available.

我想这回答了您的问题，即在尝试进行交叉验证时为什么会出现此错误.

I guess this answers your question of why you get this error when you try to have a cross-validation in training.

下面是它的工作方式:

library(caret)
library(mosaicData)

data(HELPrct)
ds = HELPrct
fitControl= trainControl(method="repeatedcv",times=5)
ds$sub = as.factor(ds$substance)

rpartFit1 <- train(homeless ~ female + i1 + sub + sexrisk + mcs + pcs, 
                   trControl=fitControl, 
                   method = "rpart", 
                   data=ds[complete.cases(ds),])

rpartFit1
CART 

117 samples
  6 predictor
  2 classes: 'homeless', 'housed' 

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 105, 105, 105, 106, 105, 106, ... 
Resampling results across tuning parameters:

  cp          Accuracy   Kappa      
  0.00000000  0.5280303  -0.03503032
  0.01190476  0.5280303  -0.03503032
  0.07142857  0.5977273  -0.02970604

Accuracy was used to select the optimal model using the largest value.
The final value used for the model was cp = 0.07142857.

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