# Is it reasonable to extract split-point locations of an RF model?

If I build a random forest model (classifier) and display the results in a partial dependence plot (PDP) and observe a clear abrupt change. Is it then reasonable to extract the split-points of every tree from a particular predictor to say something about the highest density (mean/median), with the range (i.e. 2.5% and 97.5%) accompanied by a histogram, where these split-points are placed. I want to quantify something about the observed changes in the PDP and not only describe them. Although, I understand that if I would have a non-linear response, i.e wave function, this does not really help. Yet, if I simply observe a sharp increase or decrease can this be reasonable? Furthermore, with large amount of predictors the range can be extremely large, but this is also useful information, which can indicate that the changes are less abrupt. For the data I use this seems to quite well reflect the observations in the PDPs. What are your thoughts on this?

#Something like this

set.seed(123)
data          <- data.frame(y=as.factor(c(rbinom(500, 1, .95), rbinom(500, 1, .5))), x=c(1:1000))

weight        <- min(as.data.frame(table(data$y))[2]) mod <- randomForest::randomForest(y~x, data=data, weight=weight, strat=data$y, ntree=100, nodesize=weight*.2)

par(mfrow = c(1,2))
partialPlot(mod, data, x.var = x)

splits        <- list()
for(n in 1:100){
extsplit    <- getTree(mod, k=n, labelVar = T)
ext         <- extsplit$$split point[extsplit$$split var == "x"]
splits[[n]] <- ext[!duplicated(ext)]}

splitloc1   <- do.call(rbind.data.frame, splits)
splitloc2   <- as.numeric((tidyr::gather(splitloc1)[,2]))

mean(splitloc2 ,na.rm = T)
quantile(splitloc2, c(.025, .975), na.rm = T)

hist(splitloc2)