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- library(caret)
- d<-twoClassSim(10000, intercept = -10, linearVars = 10, noiseVars = 10 )
- c<-trainControl(method="cv",summaryFunction=twoClassSummary,classProbs=T,allowParallel = F)
- train(Class~.,data=d, method="rpart", trControl=tc, tuneGrid = expand.grid(cp=c(2^-seq(1:24),0)), metric="ROC")
- CART
- 10000 samples
- 25 predictor
- 2 classes: 'Class1', 'Class2'
- No pre-processing
- Resampling: Cross-Validated (10 fold)
- Summary of sample sizes: 9001, 9001, 9000, 9000, 9000, 9000, ...
- Resampling results across tuning parameters:
- cp ROC Sens Spec
- 0.000000e+00 0.8720221 0.9175038 0.6351468
- 5.960464e-08 0.8693352 0.9178879 0.6338036
- 1.192093e-07 0.8693352 0.9178879 0.6338036
- 2.384186e-07 0.8693352 0.9178879 0.6338036
- 4.768372e-07 0.8693352 0.9178879 0.6338036
- 9.536743e-07 0.8693352 0.9178879 0.6338036
- 1.907349e-06 0.8693352 0.9178879 0.6338036
- 3.814697e-06 0.8693352 0.9178879 0.6338036
- 7.629395e-06 0.8693352 0.9178879 0.6338036
- 1.525879e-05 0.8693352 0.9178879 0.6338036
- 3.051758e-05 0.8693352 0.9178879 0.6338036
- 6.103516e-05 0.8693352 0.9178879 0.6338036
- 1.220703e-04 0.8688977 0.9184034 0.6338036
- 2.441406e-04 0.8695238 0.9190479 0.6333571
- 4.882812e-04 0.8683167 0.9199503 0.6346964
- 9.765625e-04 0.8642201 0.9234327 0.6275635
- 1.953125e-03 0.8502711 0.9358066 0.6061528
- 3.906250e-03 0.8170988 0.9421235 0.5776111
- 7.812500e-03 0.7992001 0.9391563 0.5624742
- 1.562500e-02 0.7309271 0.9416099 0.4928790
- 3.125000e-02 0.7279783 0.9249799 0.5267897
- 6.250000e-02 0.7279783 0.9249799 0.5267897
- 1.250000e-01 0.6607248 0.9497346 0.3688948
- 2.500000e-01 0.5000000 1.0000000 0.0000000
- 5.000000e-01 0.5000000 1.0000000 0.0000000
- ROC was used to select the optimal model using the largest value.
- The final value used for the model was cp = 0.0002441406.
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