# Use adaboost
library(adabag)
boost_fit <- boosting(formula, data, boos = TRUE, mfinal = 100, coeflearn = 'Breiman', control,...)

# Use xgboost
library(xgboost)
xgboost_fit <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")


cancer <- read.csv("breast_cancer.csv")

set.seed(2017)
ind <- sample(nrow(cancer),0.8*nrow(cancer))
train <- cancer[ind,]
test <- cancer[-ind,]

library(randomForest)
rf <- randomForest(diagnosis ~ .,
                   data = train[,2:10],
                   ntree = 20)
pred <- predict(rf, newdata = test[,-c(length(test))])
cm <- table(test$diagnosis, pred)
cm