Mastering Machine Learning with R - Second Edition

Classification tree

For the classification problem, we will prepare the breast cancer data in the same fashion as we did in Chapter 3, Logistic Regression and Discriminant Analysis. After loading the data, you will delete the patient ID, rename the features, eliminate the few missing values, and then create the train/test datasets in the following way:

  > data(biopsy)  > biopsy <- biopsy[, -1] #delete ID  > names(biopsy) <- c("thick", "u.size", "u.shape", "adhsn",     "s.size", "nucl",  "chrom", "n.nuc", "mit", "class") #change the feature names  > biopsy.v2 <- na.omit(biopsy) #delete the observations with     missing values  > set.seed(123) #random number generator  > ind <- sample(2, nrow(biopsy.v2), replace = TRUE, prob = c(0.7,     0.3))  > biop.train ...