Get the accuracy of a random forest in R

Question

I have created a random forest out of my data:

fit=randomForest(churn~., data=data_churn[3:17], ntree=1,
                 importance=TRUE, proximity=TRUE)

I can easily see my confusion matrix :

conf <- fit$confusion
> conf
     No Yes class.error
No  945  80  0.07804878
Yes  84 101  0.45405405

Now I need to know the accuracy for the random forest. I searched around and realized that caret library has a confusionMatrix method that gets a confusion matrix and returns the accuracy (alongside with many other values). However, the method needs another parameter called "reference" . My question is how can I provide a reference for the method to get the accuracy of my random forest? And... is it the correct way to get the accuracy of a random forest?

Answer 1

Use randomForest(..., do.trace=T) to see the OOB error during training, by both class and ntree.

(FYI you chose ntree=1 so you'll only get just one rpart decision-tree, not a forest, this kind of defeats the purpose of using RF, and of randomly choosing a subset of both features and samples. You probably want to vary ntree values.)

And after training, you can get per-class error from the rightmost column of the confusion matrix as you already found:

> fit$confusion[, 'class.error']
class.error
No         Yes
0.07804878 0.45405405

(Also you probably want to set options('digits'=3) to not see those excessive decimal places)

As to converting that list of class errors ( accuracies = 1 - errors ) to one overall accuracy number, that's easy to do. You could use mean, class-weighted mean, harmonic mean (of accuracies, not of errors) etc. It depends on your application and the relative penalty for misclassifying. Your example is simple, it's only two-class.

(or eg there are more complicated measures of inter-rater agreement)