xgboost校准曲线的R语言代码

以下是使用R语言绘制xgboost校准曲线的示例代码：

library(xgboost)
library(caret)
data(iris)

# 将鸢尾花数据集划分为训练集和测试集
set.seed(123)
trainIndex <- createDataPartition(iris$Species, p = 0.8, list = FALSE)
train <- iris[trainIndex,]
test <- iris[-trainIndex,]

# 训练xgboost模型
xgb_model <- xgboost(data = as.matrix(train[,1:4]), label = train$Species, nrounds = 50, objective = "multi:softprob", num_class = 3)

# 预测测试集
xgb_pred <- predict(xgb_model, as.matrix(test[,1:4]))

# 计算校准曲线
xgb_calib <- calibration(test$Species, xgb_pred, method = "isotonic")

# 绘制校准曲线
plot(xgb_calib, main = "XGBoost Calibration Curve", xlab = "Predicted Probability", ylab = "True Probability", ylim = c(0,1))
lines(c(0,1), c(0,1), col = "red", lty = 2)

这段代码使用鸢尾花数据集训练了一个xgboost分类器，并绘制了校准曲线。对于其他数据集，只需要将数据导入并修改相应的代码即可。