how to check variable importance in xgboost model of R language

You can use the xgb.importance function in the xgboost package of R to check the variable importance in an XGBoost model.

Here's an example code:

library(xgboost)

# load data
data(agaricus.train, package='xgboost')
train <- agaricus.train$data
labels <- agaricus.train$label

# fit xgboost model
xgb_model <- xgboost(data = train, label = labels, max_depth = 2,
                     eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")

# check variable importance
importance_matrix <- xgb.importance(model = xgb_model)
print(importance_matrix)

In this example, we first load the agaricus dataset from the xgboost package and split it into the input data and labels. Then, we fit an XGBoost model with some hyperparameters. Finally, we use the xgb.importance function to obtain the variable importance matrix of the model and print it. The importance matrix contains the feature names and their corresponding scores, where the higher score indicates more important features