# How to get class probabilities for unsupervised random forest

I have created random forest for the unsupervised case.

g = randomForest(iris[,-5],keep.forest=TRUE)


Now I need to know the class probabilities for each entry (with respect to iris$Species). In case of a supervised case, then I would use this code: p = predict(g, iris, type = "prob")  However, for the unsupervised case it says: Can't predict unsupervised forest. So, how can I extract the class probabilities? ## 1 Answer In unsupervised case randomForest produces a proximity matrix that you can use for clustering. library(randomForest) g <- randomForest(iris[,-5], keep.forest=FALSE, proximity=TRUE) mds <- MDSplot(g, iris$Species, k=2, pch=16, palette=c("skyblue", "orange", "darkblue"))
library(cluster)
clusters_pam <- pam(1-g$proximity, k=3, diss = TRUE) plot(mds$points[, 1], mds$points[, 2], pch=clusters_pam$clustering+14, col=c("skyblue", "orange", "darkblue")[as.numeric(iris$Species)]) legend("bottomleft", legend=unique(clusters_pam$clustering), pch = 15:17, title = "PAM cluster")
legend("topleft", legend=unique(iris$Species), pch = 16, col=c("skyblue", "orange", "darkblue"), title = "Iris species")  MDS stands for Multi-dimensional Scaling. Of course the clusters won't one-on-one map to original classes (that's why I deliberately didn't remap clusters - so it's not a confusion matrix: table(clusters_pam$clustering, iris$Species) setosa versicolor virginica 1 50 0 0 2 0 9 42 3 0 41 8  Two dimensional MDS plot: Then you can use your clusters as classes to train a supervised model: g_new <- randomForest(x=iris[,-5], y=as.factor(clusters_pam$clustering), keep.forest=TRUE, proximity=TRUE)
table(predict(g_new, iris[,-5]), clusters_pam$clustering) 1 2 3 1 50 0 0 2 0 51 0 3 0 0 49  For the sake of our example and because Iris dataset is so short, we generate a simulated Iris dataset: library(semiArtificial) # to generate dummy data for testing # create tree ensemble generator for classification problem irisGenerator<- treeEnsemble(Species~., iris, noTrees=100) # use the generator to create new data irisNew <- newdata(irisGenerator, size=200)  Now we can predict on the new dataset and inspect how it is in agreement with the simulated dataset's species class: table(predict(g_new, irisNew[,-5]), irisNew$Species)

setosa versicolor virginica
1     66          1         4
2      1          7        56
3      5         55         5


To predict probabilities:

predict(g_new, irisNew[,-5], type="prob")

1     2     3
1   1.000 0.000 0.000
2   0.014 0.002 0.984
3   0.000 0.000 1.000
4   1.000 0.000 0.000
5   0.020 0.068 0.912
6   0.000 1.000 0.000
7   1.000 0.000 0.000
8   0.480 0.000 0.520
9   0.526 0.000 0.474
10  1.000 0.000 0.000

• In this example you know how many species clusters there are. Can Random Forests suggest a number of clusters? Dec 1, 2017 at 19:45