I am trying to train a model using bstTree method and print out the confusion matrix. adverse_effects is my class attribute.
set.seed(1234) splitIndex <- createDataPartition(attended_num_new_bstTree$adverse_effects, p = .80, list = FALSE, times = 1) trainSplit <- attended_num_new_bstTree[ splitIndex,] testSplit <- attended_num_new_bstTree[-splitIndex,] ctrl <- trainControl(method = "cv", number = 5) model_bstTree <- train(adverse_effects ~ ., data = trainSplit, method = "bstTree", trControl = ctrl) predictors <- names(trainSplit)[names(trainSplit) != 'adverse_effects'] pred_bstTree <- predict(model_bstTree$finalModel, testSplit[,predictors]) plot.roc(auc_bstTree) conf_bstTree= confusionMatrix(pred_bstTree,testSplit$adverse_effects) But I get the error 'Error in confusionMatrix.default(pred_bstTree, testSplit$adverse_effects) : The data must contain some levels that overlap the reference.'
max(pred_bstTree) [1] 1.03385 min(pred_bstTree) [1] 1.011738 > unique(trainSplit$adverse_effects) [1] 0 1 Levels: 0 1 How can I fix this issue?
> head(trainSplit) type New_missed Therapytypename New_Diesease gender adverse_effects change_in_exposure other_reasons other_medication 5 2 1 14 13 2 0 0 0 0 7 2 0 14 13 2 0 0 0 0 8 2 0 14 13 2 0 0 0 0 9 2 0 14 13 2 1 0 0 0 11 2 1 14 13 2 0 0 0 0 12 2 0 14 13 2 0 0 0 0 uvb_puva_type missed_prev_dose skintypeA skintypeB Age DoseB DoseA 5 5 1 1 1 22 3.000 0 7 5 0 1 1 22 4.320 0 8 5 0 1 1 22 4.752 0 9 5 0 1 1 22 5.000 0 11 5 1 1 1 22 5.000 0 12 5 0 1 1 22 5.000 0 2 Answers
I had similar problem, which refers to this error. I used function confusionMatrix:
confusionMatrix(actual, predicted, cutoff = 0.5)
An I got the following error: Error in confusionMatrix.default(actual, predicted, cutoff = 0.5) : The data must contain some levels that overlap the reference.
I checked couple of things like:
class(actual) -> numeric
class(predicted) -> integer
unique(actual) -> plenty values, since it is probability
unique(predicted) -> 2 levels: 0 and 1
I concluded, that there is problem with applying cutoff part of the function, so I did it before by:
predicted<-ifelse(predicted> 0.5,1,0)
and run the confusionMatrix function, which works now just fine:
cm<- confusionMatrix(actual, predicted) cm$table
which generated correct outcome.
One takeaway for your case, which might improve interpretation once you make code working: you mixed input values for your confusion matrix(as per confusionMatrix package documetation), instead of:
conf_bstTree= confusionMatrix(pred_bstTree,testSplit$adverse_effects)
you should have written:
conf_bstTree= confusionMatrix(testSplit$adverse_effects,pred_bstTree)
As said it will most likely help you interpret confusion matrix, once you figure out way to make it work.
Hope it helps.
max(pred_bstTree) [1] 1.03385
min(pred_bstTree) [1] 1.011738
and errors tells it all. Plotting ROC is simply checking the effect of different threshold points. Based on threshold rounding happens e.g. 0.7 will be converted to 1 (TRUE class) and 0.3 will be go 0 (FALSE class); in case threshold is 0.5. Threshold values are in range of (0,1)
In your case regardless of threshold you will always get all observations into TRUE class as even minimum prediction is greater than 1. (Thats why @phiver was wondering if you are doing regression instead of classification) . Without any zero in prediction there is no level in 'prediction' which coincide with zero level in adverse_effects and hence this error.
PS: It will be difficult to tell root cause of error without you posting your data
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