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dataPath <- "data/"
train <- read.csv(paste0(dataPath,"train_sample.csv"))
test <- read.csv(paste0(dataPath,"test_sample.csv"))

m=8 # = ncol(train)
predictors = paste0("X",1:(m-1))

library(xgboost)
library(randomForest)

param<-list("objective" = "binary:logistic")

matrHitters = data.matrix(train)[,predictors]
target = train$class

set.seed(1)
cvSalary = xgb.cv(params=param, data = matrHitters, label = target,
                nfold=5, nrounds = 50,prediction=T,verbose=F)

set.seed(1)
(cvSalary.table<-cvSalary$evaluation_log)

(bestNR = which.min(cvSalary.table$test_error_mean))

[1] 6

set.seed(1)
modelSalary<-xgboost(data=matrHitters, label = target, params=param,
                     nrounds=bestNR,verbose=F,save_period=NULL)

predicts <- predict(modelSalary, newdata=data.matrix(test)[,predictors])
pred <- cbind(test$id, as.integer(predicts > 0.5))

set.seed(1)
rfclass <- randomForest(x=train, y=as.factor(train$class), importance=T)

importance(rfclass)

               0          1 MeanDecreaseAccuracy MeanDecreaseGini
X1     2.8238562  2.5283712             3.618061         4.943689
X2    13.9384582 14.5884319            18.415655        75.076616
X3     3.9887162  1.6126005             4.132744         4.445975
X4     1.5982712  0.4250877             1.446682         4.455088
X5     1.0261508 -2.4955707            -1.149475         2.864263
X6    -0.2342377  4.9677834             3.705306         5.799035
X7     0.7290124  4.0690887             3.445250         4.058694
class 61.8332416 62.0669279            72.036835       134.213192

importance(rfclass)[which.max(importance(rfclass)[,"MeanDecreaseAccuracy"])]

[1] 61.83324

most = 2

saveRDS(list(RFMostImportant = most, Forecast = pred), "W4answer.rds")

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