R caret inconsistent results in model tuning - r

Question

Using the caret package for model tuning today I have faced this strange behavior: given a specific combination of tuning parameters T*, the metric (i.e. Cohen's K) value associated with T* changes if T* is evaluated alone or as part of a grid of possible combinations. In the practical example which follows caret is used to interface with the gbm package.
# Load libraries and data
library (caret)
data<-read.csv("mydata.csv")
data$target<-as.factor(data$target)
# data are available at https://www.dropbox.com/s/1bglmqd14g840j1/mydata.csv?dl=0
Pocedure 1: T* evaluated alone
#Define 5-fold cv as validation settings
fitControl <- trainControl(method = "cv",number = 5)
# Define the combination of tuning parameter for this example T*
gbmGrid <- expand.grid(.interaction.depth = 1,
.n.trees = 1000,
.shrinkage = 0.1, .n.minobsinnode=1)
# Fit a gbm with T* as model parameters and K as scoring metric.
set.seed(825)
gbmFit1 <- train(target ~ ., data = data,
method = "gbm",
distribution="adaboost",
trControl = fitControl,
tuneGrid=gbmGrid,
verbose=F,
metric="Kappa")
# The results show that T* is associated with Kappa = 0.47. Remember this result and the confusion matrix.
testPred<-predict(gbmFit1, newdata = data)
confusionMatrix(testPred, data$target)
# output selection
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 832 34
1 0 16
Kappa : 0.4703
Procedure 2: T* evaluated along with other tuning profiles
Here everything is the same as in procedure 1 except for the fact that several combinations of tuning parameters {T} are considered:
# Notice that the original T* is included in {T}!!
gbmGrid2 <- expand.grid(.interaction.depth = 1,
.n.trees = seq(100,1000,by=100),
.shrinkage = 0.1, .n.minobsinnode=1)
# Fit the gbm
set.seed(825)
gbmFit2 <- train(target ~ ., data = data,
method = "gbm",
distribution="adaboost",
trControl = fitControl,
tuneGrid=gbmGrid2,
verbose=F,
metric="Kappa")
# Caret should pick the model with the highest Kappa.
# Since T* is in {T} I would expect the best model to have K >= 0.47
testPred<-predict(gbmFit2, newdata = data)
confusionMatrix(testPred, data$target)
# output selection
Reference
Prediction 0 1
0 831 47
1 1 3
Kappa : 0.1036
The results are inconsistent with my expectations: the best model in {T} scores K=0.10. How is it possible given that T* has K = 0.47 and it is included in {T}? Additionally, according to the following plot , K for T* as evaluated in procedure 2 is now around 0.01. Any idea about what is going on? Am I missing something?

Answer 1

I am getting consistent resampling results from your data and code.
The first model has Kappa = 0.00943
gbmFit1$results
interaction.depth n.trees shrinkage n.minobsinnode Accuracy Kappa AccuracySD
1 1 1000 0.1 1 0.9331022 0.009430576 0.004819004
KappaSD
1 0.0589132
The second model has the same results for n.trees = 1000
gbmFit2$results
shrinkage interaction.depth n.minobsinnode n.trees Accuracy Kappa AccuracySD
1 0.1 1 1 100 0.9421803 -0.002075765 0.002422952
2 0.1 1 1 200 0.9387776 -0.008326896 0.002468351
3 0.1 1 1 300 0.9365049 -0.012187900 0.002625886
4 0.1 1 1 400 0.9353749 -0.013950906 0.003077431
5 0.1 1 1 500 0.9353685 -0.013961221 0.003244201
6 0.1 1 1 600 0.9342322 -0.015486214 0.005202656
7 0.1 1 1 700 0.9319658 -0.018574633 0.007033402
8 0.1 1 1 800 0.9319658 -0.018574633 0.007033402
9 0.1 1 1 900 0.9342386 0.010955568 0.003144850
10 0.1 1 1 1000 0.9331022 0.009430576 0.004819004
KappaSD
1 0.004641553
2 0.004654972
3 0.003978702
4 0.004837097
5 0.004878259
6 0.007469843
7 0.009470466
8 0.009470466
9 0.057825336
10 0.058913202
Note that the best model in your second run has n.trees = 900
gbmFit2$bestTune
n.trees interaction.depth shrinkage n.minobsinnode
9 900 1 0.1 1
Since train picks the "best" model based on your metric, your second prediction is using a different model (n.trees of 900 instead of 1000).