breast_cancer_modeling.r

library(caret)
library(mlbench)

data(BreastCancer)
summary(BreastCancer) #Summary of Dataset

df <- BreastCancer
# convert input values to numeric
for(i in 2:10) {
  df[,i] <- as.numeric(as.character(df[,i]))
}

# split the data into train and test and perform preprocessing
trainIndex <- createDataPartition(df$Class, p = .8, 
                                  list = FALSE, 
                                  times = 1)
df_train <- df[ trainIndex,]
df_test  <- df[-trainIndex,]
preProcValues <- preProcess(df_train, method = c("center", "scale", "medianImpute"))
df_train_transformed <- predict(preProcValues, df_train)

# train a model on df_train
fitControl <- trainControl(## 10-fold CV
  method = "repeatedcv",
  number = 10,
  ## repeated ten times
  repeats = 10,
  ## Estimate class probabilities
  classProbs = TRUE,
  ## Evaluate performance using 
  ## the following function
  summaryFunction = twoClassSummary)

set.seed(825)
gbmFit <- train(Class ~ ., data = df_train_transformed[,2:11], 
                method = "gbm", 
                trControl = fitControl,
                ## This last option is actually one
                ## for gbm() that passes through
                verbose = FALSE,
                metric = "ROC")
gbmFit

saveRDS(preProcValues, file = './preProcessor.rds')
saveRDS(gbmFit, file = './gbm_model.rds')
saveRDS(df_test[,1:10], file = './breast_cancer_test_data.rds')