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We know that randomForest is a classification model. Let's say I have 2 classes A, B , and after run randomForest model (particularly I used R, but it does not matter).

Assume that I have a result:

  A B
A 29 2
B 3 40

The columns are prediction and rows are actual classes.

My question is, could I get the matching score of each element. For instance, for 32 elements which are predicted 'A', could I rank them based on matching score, from the element that is predicted highest match to A, to the lowest match.

I am sorry if my English explanation is not very clear. Let me know if it is the case and I will try to explain more details.

(The question is: randomForest predicted 32 items as class A, and 42 items as class B, but it does not tell me how much score it gives for each item. For instance, I hope that I can have some information like: the model predict item_1 with 98% belongs to class A, and item_2 with 90% belongs to class A, and so on ...)

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You can use votes, i.e., the per cent of trees in the forest that voted for a given class. OOB votes are stored in the $votes element of the randomForest object:

> randomForest(Species~.,data=iris)$votes
         setosa  versicolor   virginica
1   1.000000000 0.000000000 0.000000000
 ...
15  0.979274611 0.020725389 0.000000000
 ...
58  0.005780347 0.745664740 0.248554913

In case of predicting new data, use type="votes" switch in the predict method.

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  • $\begingroup$ The answer is great @mbq, but is there a way when I get the votes for the predicted class (for instance, in your example, the element 1, I just want to get the vote for setosa, so I will have a dataset like: 1 setosa 1.00 ... 15 setosa 0.979 .... 58 versicolor 0.746 $\endgroup$ – mamatv Nov 18 '15 at 10:05
  • $\begingroup$ That's just max of each row, apply(<votes array>,1,max) will do the trick. You can use standard prediction to append predicted class name. Note that tie-breaking may be inconsistent, but it is very rare. $\endgroup$ – user88 Nov 18 '15 at 14:00

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