r - How to use trained caret object to predict on new data (not used while training)? -

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i using caret package train random forest model on training dataset. have used 10-fold cross validation object randomforestfit. use object predict on new data set test_data. want respective class probabilities. how that?

i have been using extractprob function follows :

extractprob(randomforestfit, textx = test_data_predictors, testy = test_data_labels)

but it's giving me unexpected results.

from extractprob page example, need wrap model in list:

knnfit <- train(species ~ ., data = iris, method = "knn",                  trcontrol = traincontrol(method = "cv"))  rdafit <- train(species ~ ., data = iris, method = "rda",                  trcontrol = traincontrol(method = "cv"))  predict(knnfit) predict(knnfit, type = "prob")  bothmodels <- list(knn = knnfit,                    tree = rdafit)  predict(bothmodels)  extractprediction(bothmodels, testx = iris[1:10, -5]) extractprob(bothmodels, testx = iris[1:10, -5])

so following should work:

extractprob(list(randomforestfit), textx = test_data_predictors, testy = test_data_labels)

edit:

and yes, preprocessing used. documentation:

these processing steps applied during predictions generated using predict.train, extractprediction or extractprobs (see details later in document). pre-processing not applied predictions directly use object$finalmodel object.


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