I am trying to train a xgboost model using iris dataset. The training code is shown below, and both prediction functions produce the same results. However, the length of the results is 135, while test_data has only 45 rows. In addition, the results seems to look like predicted probabilities, but there are 3 classes in the label, while the results only produce a vector instead of a matrix of predicted probabilities of three classes. So, how can I get the predicted probability for each class and also the predicted class?

data("iris")
iris$Species <- as.numeric(as.factor(iris$Species)) - 1

indexes <- caret::createDataPartition(iris$Species, p = .7, list = F)
train_data <- iris[indexes, ]
test_data <- iris[-indexes, ]

xgb.train <- xgb.DMatrix(data = as.matrix(train_data), label = train_data$Species)
xgb.test <- xgb.DMatrix(data = as.matrix(test_data), label = test_data$Species)

params = list("objective" = "multi:softprob", 
              "eval_metric" = "mlogloss",
              "num_class" = 3)

xgb.model <- xgboost::xgb.train(params = params, data = xgb.train, nrounds = 1000)
predict(xgb.model, newdata = xgb.test)
predict(xgb.model, newdata = xgb.test, type = "prob")

0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.985415220 0.008038994 0.006545801 0.985415220 0.008038994 0.006545801
0.977108896 0.016400522 0.006490625 0.985415220 0.008038994 0.006545801
0.008124468 0.983585954 0.008289632 0.005110676 0.989674747 0.005214573
0.003452316 0.993025184 0.003522499 0.005499140 0.988889933 0.005610934
0.011182932 0.977406859 0.011410273 0.005110676 0.989674747 0.005214573
0.011182932 0.977406859 0.011410273 0.011182932 0.977406859 0.011410273
0.003452316 0.993025184 0.003522499 0.010401487 0.978985548 0.010612942
0.005250969 0.005771303 0.988977730 0.005250969 0.005771303 0.988977730
0.005250969 0.005771303 0.988977730 0.005239322 0.007976402 0.986784279
0.005239322 0.007976402 0.986784279 0.005239322 0.007976402 0.986784279
0.005250969 0.005771303 0.988977730 0.005219116 0.011802264 0.982978642
0.005250969 0.005771303 0.988977730 0.005219116 0.011802264 0.982978642
0.005219116 0.011802264 0.982978642 0.005250969 0.005771303 0.988977730
0.005250969 0.005771303 0.988977730 0.005250969 0.005771303 0.988977730
0.005180326 0.019146746 0.975672841