Displaying 1 result from an estimated 1 matches for "controltrainingset".
2017 Dec 02
0
How can you find the optimal number of values to randomly sample to optimize random forest classification without trial and error?
...rop.table(table(factor(
control_s[[i]], levels = 1:100
))))[,2]}
patientfreq <- list()for (i in 1:length(patient_s)){
patientfreq[[i]] <-
as.data.frame(prop.table(table(factor(
patient_s[[i]], levels = 1:100
))))[,2]}
controlfreq <- t(as.data.frame(controlfreq))
controltrainingset <- transform(controlfreq, status = "control")
patientfreq <- t(as.data.frame(patientfreq))
patienttrainingset <- transform(patientfreq, status = "patient")
dataset <- rbind(controltrainingset, patienttrainingset)
This is the final data frame being used in the class...