erickenjilee / physmap_manuscript Goto Github PK

Hello! First of all, congrats on the amazing manuscript. I had a question about differences between the paper and the codebase. In the paper, you write:

We conducted an 15-85% test-train split and trained a gradient boosted tree model (GBM) with five-fold cross-validation on this dataset to identify one of five cell types that had over 25 examples each and this process was repeated 10 times with different random seeds

However, in the codebase, these are the following lines of code:

i <- createDataPartition(E$origCells, times = 1, p = 0.7, list = FALSE)
training = E[i[,1],]
testingset = E[-i[,1],]
   
ctrl <- trainControl(method = "repeatedcv", number=numreps)
#fit a regression model and use k-fold CV to evaluate performance
model <- train(origCells~., data = training, method = "rf", trControl = ctrl, verbose=FALSE)

where numreps=2. It seems the train-test split is 70-30 (not 85-15) and you are using 2-fold validation (not 5-fold validation). Can you help us understand these differences?