CODEWITHSUNDEEP
rrandom-forest
spektra
spektra

Reputation: 407

How to call randomForest predict for use with ROCR?

I am having a hard time understanding how to build a ROC curve and now I came to the conclusion that maybe I don't create the model correctly. I am running a randomforest model in the dataset where the class attribute "y_n" is 0 or 1. I have divided the datasets as bank_training and bank_testing for the prediction purpose. Here are the steps i do:

bankrf <- randomForest(y_n~., data=bank_training, mtry=4, ntree=2,
                  keep.forest=TRUE, importance=TRUE)
bankrf.pred <- predict(bankrf, bank_testing, type='response',
                  predict.all=TRUE, norm.votes=TRUE)

Is it correct what I do till now? The bankrf.pred object that is created is a list object with 2 classes named: aggregate and individuals. I dont understand where did this 2 class names came out? Moreover when I run:

summary(bankrf.pred)
           Length Class  Mode     
aggregate  22606  factor numeric  
individual 45212  -none- character

What does this summary mean? The datasets (training & testing) are 22605 and 22606 long each. If someone can explain me what is happening I would be very grateful. I think there is something wrong in all this.

When I try to design the ROC curve with ROCR I use the following code:

library(ROCR)
pred <- prediction(bank_testing$y_n, bankrf.pred$c(0,1))

Error in is.data.frame(labels) : attempt to apply non-function

Is just a mistake in the way I try to create the ROC curve or is it from the beginning with randomForest?

Upvotes: 0

Views: 3265

Answers (2)

einrogerst
einrogerst

Reputation: 36

You should erase the predict.all=TRUE argument from predict if you simply want to get the predicted classes. By using predict.all=TRUE you are telling the function to keep the predictions of all trees rather than the prediction from the forest.

Upvotes: 0

joran
joran

Reputation: 173527

The documentation for the function you are attempting to use includes this description of its two main arguments:

predictions A vector, matrix, list, or data frame containing the predictions.

labels A vector, matrix, list, or data frame containing the true class labels. Must have the same dimensions as 'predictions'.

You are currently passing the variable y_n to the predictions argument, and what looks to me like nonsense to the labels argument.

The predictions will be stored in the output of the random forest model. As documented at ?predict.randomForest, it will be a list with two components. aggregate will contain the predicted values for the entire forest, while individual will contain the predicted values for each individual tree.

So you probably want to do something like this:

predictions(bankrf.pred$aggregate, bank_testing$y_n)

See how that works? The predicted values are passed to the predictions argument, while the "labels" or true values, are passed to the labels argument.

Upvotes: 2

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