Diabetes is a health hazard, which is mostly incurable, and patients who are diagnosed with it have to adjust their lifestyles in order to cater to this condition. Based on variables such as pregnant, glucose, pressure, triceps, insulin, mass, pedigree, and age, the problem here is to classify the person as diabetic or not. Here, we have 768 observations. This dataset is drawn from the mlbench package:

> data("PimaIndiansDiabetes")
> set.seed(12345)
> Train_Test <- sample(c("Train","Test"),nrow(PimaIndiansDiabetes),replace = TRUE,
+ prob = c(0.7,0.3))
> head(Train_Test)
[1] "Test"  "Test"  "Test"  "Test"  "Train" "Train"
> PimaIndiansDiabetes_Train <- PimaIndiansDiabetes[Train_Test=="Train",]
> PimaIndiansDiabetes_TestX <- within(PimaIndiansDiabetes[Train_Test=="Test",],
+                                     rm(diabetes))
> PimaIndiansDiabetes_TestY <- PimaIndiansDiabetes[Train_Test=="Test","diabetes"]
> PID_Formula <- as.formula("diabetes~.")

The five datasets described up to this point are classification problems. We look at one example each for regression, time series, survival, clustering, and outlier detection problems.