Confusion matrix with different levels

Question

I want to print a confusion matrix, but data and reference have not the same level. How can I do?

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 = 79,
                   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)
confusionMatrix (xgbpred, ts_label)

Answer 1

You need to convert the numeric vectors to factors, for example like this:

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)
Confusion Matrix and Statistics

          Reference
Prediction 0 1 2
         0 4 4 0
         1 0 1 6
         2 0 0 0