怎么用R语言寻找XGBoost的最优的n_estimator参数
时间: 2024-05-09 19:20:16 浏览: 12
要寻找XGBoost的最优的n_estimator参数,可以使用交叉验证来进行模型的评估和调优。具体步骤如下:
1.导入数据集
首先需要导入数据集,此处以iris数据集为例:
```r
library(xgboost)
data(iris)
train <- iris[,1:4]
label <- iris[,5]
```
2.设置参数空间
设置需要调整的参数空间,此处只调整n_estimator参数:
```r
param <- list(
objective = "multi:softmax",
eval_metric = "mlogloss",
num_class = 3,
nthread = 4,
booster = "gbtree",
eta = 0.1,
max_depth = 6,
min_child_weight = 1,
subsample = 0.8,
colsample_bytree = 0.8,
seed = 123
)
n_est <- c(50, 100, 150, 200, 250, 300, 350, 400, 450, 500)
```
3.交叉验证
使用xgb.cv函数进行交叉验证,评估每个n_estimator参数的性能:
```r
cv.n <- xgb.cv(
params = param,
data = xgb.DMatrix(train, label = label),
nrounds = 1000,
nfold = 5,
stratified = TRUE,
early_stopping_rounds = 10,
verbose = 0,
print_every_n = 10,
maximize = FALSE,
metrics = "mlogloss",
prediction = TRUE,
seed = 123,
callbacks = list(print_evaluation = function(env) {
score <- env$evaluation_log$test_mlogloss_mean
msg <- paste("CV-MLogLoss:", round(tail(score, 1), 5))
message(msg)
})
)
```
4.选择最优参数
根据交叉验证的结果,选择最优的n_estimator参数:
```r
best.n <- which.min(cv.n$evaluation_log$test_mlogloss_mean)
best.n.est <- cv.n$best_iteration
```
最后,best.n.est即为最优的n_estimator参数值。