Though in principle it should be anycodings_r similar to OOB, you should be aware of anycodings_r the differences.Ī evaluation with a better picture on anycodings_r the error might be to run tuneRF a few anycodings_r rounds, and we can use cv in caret: library(randomForest) In caret, you actually do anycodings_r cross-validation, so the test data from anycodings_r the fold was not used at all in the anycodings_r model. For anycodings_r each value of mtry, you have one score anycodings_r (or RMSE value) and this will change anycodings_r with different runs. tuneRF anycodings_r then takes the lowest OOB error. There are a few differences, for each anycodings_r mtry parameters, tuneRF fits one model anycodings_r on the whole dataset, and you get the anycodings_r OOB error from each of these fit. TuneGrid=tunegrid, ntree = 100, trControl=control) TuneRF: with this approach I'm getting the anycodings_random-forest best mtry is 3 t <- tuneRF(train, train,Ĭaret: With this approach I'm always getting anycodings_random-forest that the best mtry is all variables in this anycodings_random-forest case 6 control <- trainControl(method="cv", number=5)Ĭustom <- train(CRTOT_03~., data=train, method="rf", metric="rmse", The anycodings_random-forest question is how do I know which is the best anycodings_random-forest approach and base on what? I'm not clear if anycodings_random-forest I should expect similar or different anycodings_random-forest results. The issue is anycodings_random-forest that I'm tunning to get mtry and I'm getting anycodings_random-forest different results for each approach. I've trying to tune a random forest model anycodings_random-forest using the tuneRF tool included in the anycodings_random-forest randomForest Package and I'm also using the anycodings_random-forest caret package to tune my model.
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