I write a code in Rstudio with xgboost to solve a Machine Learning problem. This is my actual code:

```
library(xgboost)
library(tidyverse)
library(caret)
library(readxl)
library(data.table)
library(mlr)
data <- iris
righe_train <- sample(nrow(data),nrow(data)*0.8)
train <- data[righe_train,]
test <- data[-righe_train,]
setDT(train)
setDT(test)
labels <- train$Species
ts_label <- test$Species
new_tr <- model.matrix(~.+0,data = train[,-c("Species"),with=F])
new_ts <- model.matrix(~.+0,data = test[,-c("Species"),with=F])
#convert factor to numeric
labels <- as.numeric(labels)-1
ts_label <- as.numeric(ts_label)-1
#preparing matrix
dtrain <- xgb.DMatrix(data = new_tr,label = labels)
dtest <- xgb.DMatrix(data = new_ts,label=ts_label)
#default parameters
params <- list(booster = "gbtree",
objective = "multi:softmax",
num_class = 3,
eta=0.3,
gamma=0,
max_depth=6,
min_child_weight=1,
subsample=1,
colsample_bytree=1)
xgbcv <- xgb.cv( params = params,
data = dtrain,
nrounds = 100,
nfold = 5,
showsd = T,
stratified = T,
print_every_n = 10,
early_stopping_round = 20,
maximize = F)
##best iteration = 79
min(xgbcv$test.error.mean)
#first default - model training
xgb1 <- xgb.train (params = params,
data = dtrain,
nrounds = 21,
watchlist = list(val=dtest,train=dtrain),
print.every.n = 10,
early.stop.round = 10,
maximize = F ,
merror = "error")
# eval_metric = "error")
#model prediction
xgbpred <- predict (xgb1,dtest)
xgbpred <- ifelse (xgbpred > 0.5,1,0)
#confusion matrix
library(caret)
factors_both <- as.factor(c(xgbpred, ts_label))
xgbpred_f <- factors_both[1:length(xgbpred)]
ts_label_f <- factors_both[length(xgbpred)+1:length(xgbpred)*2]
confusionMatrix (xgbpred_f,ts_label_f)
#Accuracy - 86.54%`
#view variable importance plot
mat <- xgb.importance (feature_names = colnames(new_tr),model = xgb1)
xgb.plot.importance (importance_matrix = mat[1:20])
```

So, this is a Machine Learning supervised models. How can I classify a new registration? I have this new registration:

```
new_record <- c(5.3,3.2,2.0,0.2)
```

How can I classify it using the previous model?