多类别分类的SHAPforxgboost问题：；colnames中的错误；

我正在try 计算我的xgBoost模型的Shap值:

X_train <- as.matrix(train_data[, !names(train_data) %in% c("min_column")])
Y_train<-train_data$min_column
Y_train <- as.integer(as.factor(Y_train)) - 1
Y_train = as.matrix(Y_train)
num_class <- length(unique(Y_train)

> params <- list(booster = "gbtree", 
                 objective = "multi:softmax",
                 eta=0.01, 
                 gamma=0.01, 
                 max_depth=2, 
                 subsample=1, 
                 num_class=num_class)

> xgb1 <- xgb.train(data = X_train, label = Y_train, verbose = FALSE, params = params, nrounds = 10)


> shap_values <- SHAPforxgboost::shap.values(xgb_model = xgb1, X_train = X_train)

但是，我总是收到以下错误:

Error in `colnames<-`(`*tmp*`, value = c(colnames(X_train), "BIAS")) : 
      attempt to set 'colnames' on an object with less than two dimensions

我发现，当我没有在参数列表中指定与多类分类模型(booster = "gbtree" + objective = "multi:softmax"+ num_class)相关的参数时，shap.Values函数就会起作用.

但如果我不在参数中指定它，我不确定模型是否会识别我想要的多类分类模型.

有没有人知道该怎么做？