Hey guys i need your help for a university project. The main Task is to analyze the effects of over/under-smapling on a imbalanced Dataset. But before we can even start with that, our task sheet says, that we 1) have to find/create imbalanced Datasets and 2) fit those with a binary classification model like CART. So my auestions would be, where do i find such imbalanced datasets? And how do i fit those datasets with CART, and what does that help in regard of over/under-sampling?

Thats my whole first try.

 # CART - Datensatz laden
 add <- "data1.csv"
 df <- read.csv(add)
 head(df) # Ersten 6 Zeilen
 nrow(df) # Anzahl der Reihen des Datensatzes

 # CART - Wichtige Daten selektieren
 df <- mutate(df, x= as.numeric(x), y= as.numeric(y), label=factor(label))
 sample = sample.split(df$x, SplitRatio = 0.70)
 train = subset(df, sample==TRUE)
 test = subset(df, sample==FALSE)

 # grow tree (Baum wachsen lassen)
 fit <- rpart(x~., data = train, method = "class")

 # plot tree
 plot(fit, uniform = TRUE, main="Bla Bla Bla")
 # text(fit, use.n=TRUE, all=TRUE, cex=.08)

 # prune the table --> to avoid overfitting the data#
 pfit<- prune(fit, cp=   fit$cptable[which.min(fit$cptable[,"xerror"]),"CP"])
 plot(pfit, uniform=TRUE,
 main="Pruned Classification Tree for Us")

Why do i need to make such a decision tree and how does it help with Over/Under-Sampling?

Help is much appreciated


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.