我一直在研究R中的xgboost包，并进行了几个演示和教程，但仍然感到困惑：在使用xgb.cv进行交叉验证后，最佳参数是如何传递给xgb.train的？还是应该根据xgb.cv的输出计算理想参数（例如nround，max.depth）？
param <- list("objective" = "multi:softprob",
"eval_metric" = "mlogloss",
"num_class" = 12)
cv.nround <- 11
cv.nfold <- 5
mdcv <-xgb.cv(data=dtrain,params = param,nthread=6,nfold = cv.nfold,nrounds = cv.nround,verbose = T)
md <-xgb.train(data=dtrain,params = param,nround = 80,watchlist = list(train=dtrain,test=dtest),nthread=6)
看起来你误解了xgb.cv，它不是一个参数搜索函数。它只是进行了k折交叉验证，仅此而已。
在你的代码中，它没有改变param的值。
要在R的XGBoost中找到最佳参数，有一些方法。以下是两种方法：
(1) 使用mlr包，http://mlr-org.github.io/mlr-tutorial/release/html/
Kaggle的Prudential挑战赛中有一个XGBoost + mlr 示例代码，但该代码是用于回归问题而非分类问题。据我所知，mlr包中还没有mlogloss指标，因此你必须自己从头编写mlogloss度量。如有错误请指出。
(2) 第二种方法是手动设置参数并重复实验，例如：
param <- list(objective = "multi:softprob",
eval_metric = "mlogloss",
num_class = 12,
max_depth = 8,
eta = 0.05,
gamma = 0.01,
subsample = 0.9,
colsample_bytree = 0.8,
min_child_weight = 4,
max_delta_step = 1
)
cv.nround = 1000
cv.nfold = 5
mdcv <- xgb.cv(data=dtrain, params = param, nthread=6,
nfold=cv.nfold, nrounds=cv.nround,
verbose = T)
min_logloss = min(mdcv[, test.mlogloss.mean])
min_logloss_index = which.min(mdcv[, test.mlogloss.mean])
min_logloss是mlogloss的最小值，而min_logloss_index是索引（轮数）。
您必须重复上述过程多次，每次手动更改参数（mlr为您重复）。直到最终获得最佳全局最小min_logloss。
注意：您可以在100或200次迭代的循环中执行此操作，在每次迭代中随机设置参数值。这样，您必须将最佳的[parameters_list，min_logloss，min_logloss_index]保存在变量或文件中。
注意：最好通过set.seed()设置随机种子以获得可重复的结果。不同的随机种子会产生不同的结果。因此，您必须将[parameters_list，min_logloss，min_logloss_index，seednumber]保存在变量或文件中。
假设您最终在3次迭代/重复中获得了3个结果：
min_logloss = 2.1457, min_logloss_index = 840
min_logloss = 2.2293, min_logloss_index = 920
min_logloss = 1.9745, min_logloss_index = 780
然后您必须使用第三个参数（它具有全局最小值min_logloss为1.9745）。 您的最佳指数（nrounds）为780。
一旦您获得了最佳参数，请在训练中使用它，
# best_param is global best param with minimum min_logloss
# best_min_logloss_index is the global minimum logloss index
nround = 780
md <- xgb.train(data=dtrain, params=best_param, nrounds=nround, nthread=6)
watchlist，因为你已经进行了交叉验证。但如果你仍想使用watchlist，那也可以。xgb.cv中使用早停。mdcv <- xgb.cv(data=dtrain, params=param, nthread=6,
nfold=cv.nfold, nrounds=cv.nround,
verbose = T, early.stop.round=8, maximize=FALSE)
mlogloss 值在 8 步内没有下降时，xgb.cv 将停止。您可以节省时间。您必须将 maximize 设置为 FALSE，因为您期望最小化 mlogloss。best_param = list()
best_seednumber = 1234
best_logloss = Inf
best_logloss_index = 0
for (iter in 1:100) {
param <- list(objective = "multi:softprob",
eval_metric = "mlogloss",
num_class = 12,
max_depth = sample(6:10, 1),
eta = runif(1, .01, .3),
gamma = runif(1, 0.0, 0.2),
subsample = runif(1, .6, .9),
colsample_bytree = runif(1, .5, .8),
min_child_weight = sample(1:40, 1),
max_delta_step = sample(1:10, 1)
)
cv.nround = 1000
cv.nfold = 5
seed.number = sample.int(10000, 1)[[1]]
set.seed(seed.number)
mdcv <- xgb.cv(data=dtrain, params = param, nthread=6,
nfold=cv.nfold, nrounds=cv.nround,
verbose = T, early.stop.round=8, maximize=FALSE)
min_logloss = min(mdcv[, test.mlogloss.mean])
min_logloss_index = which.min(mdcv[, test.mlogloss.mean])
if (min_logloss < best_logloss) {
best_logloss = min_logloss
best_logloss_index = min_logloss_index
best_seednumber = seed.number
best_param = param
}
}
nround = best_logloss_index
set.seed(best_seednumber)
md <- xgb.train(data=dtrain, params=best_param, nrounds=nround, nthread=6)
min_logloss的迭代中。如果发现太早停止，请增加early.stop.round的值。还需要根据数据特征更改随机参数值的限制。对于100或200次迭代，您可能希望将verbose更改为FALSE。顺便说一下，这是随机方法的示例，您可以通过贝叶斯优化等方式进行调整以获得更好的方法。如果您有XGBoost的Python版本，则可以使用https://github.com/mpearmain/BayesBoost进行良好的超参数脚本搜索以获取最佳参数设置。
编辑：我想在 Kaggle论坛上添加第三种手动方法，由 Kaggle 大师“Davut Polat”发布。
编辑：如果您了解 Python 和 sklearn，您还可以使用 GridSearchCV 与 xgboost.XGBClassifier 或 xgboost.XGBRegressor。
xgboost的新手，这非常有帮助。谢谢。随机化和边界比较的方法非常鼓舞人心。易于使用且好知道。现在在2018年需要进行一些轻微的修订，例如early.stop.round应该改为early_stopping_rounds。输出mdcv的组织略有不同： min_rmse_index <- mdcv$best_iteration
min_rmse <- mdcv$evaluation_log[min_rmse_index]$test_rmse_mean
根据应用程序（线性，逻辑等...）的不同，目标，评估指标和参数也需要相应地进行调整。
为了方便任何运行回归的人，这里是略微调整过的代码版本（大部分与上述相同）。
library(xgboost)
# Matrix for xgb: dtrain and dtest, "label" is the dependent variable
dtrain <- xgb.DMatrix(X_train, label = Y_train)
dtest <- xgb.DMatrix(X_test, label = Y_test)
best_param <- list()
best_seednumber <- 1234
best_rmse <- Inf
best_rmse_index <- 0
set.seed(123)
for (iter in 1:100) {
param <- list(objective = "reg:linear",
eval_metric = "rmse",
max_depth = sample(6:10, 1),
eta = runif(1, .01, .3), # Learning rate, default: 0.3
subsample = runif(1, .6, .9),
colsample_bytree = runif(1, .5, .8),
min_child_weight = sample(1:40, 1),
max_delta_step = sample(1:10, 1)
)
cv.nround <- 1000
cv.nfold <- 5 # 5-fold cross-validation
seed.number <- sample.int(10000, 1) # set seed for the cv
set.seed(seed.number)
mdcv <- xgb.cv(data = dtrain, params = param,
nfold = cv.nfold, nrounds = cv.nround,
verbose = F, early_stopping_rounds = 8, maximize = FALSE)
min_rmse_index <- mdcv$best_iteration
min_rmse <- mdcv$evaluation_log[min_rmse_index]$test_rmse_mean
if (min_rmse < best_rmse) {
best_rmse <- min_rmse
best_rmse_index <- min_rmse_index
best_seednumber <- seed.number
best_param <- param
}
}
# The best index (min_rmse_index) is the best "nround" in the model
nround = best_rmse_index
set.seed(best_seednumber)
xg_mod <- xgboost(data = dtest, params = best_param, nround = nround, verbose = F)
# Check error in testing data
yhat_xg <- predict(xg_mod, dtest)
(MSE_xgb <- mean((yhat_xg - Y_test)^2))
我发现Silo的回答非常有帮助。除了他的随机研究方法之外，您可能还想使用贝叶斯优化来促进超参数搜索的过程，例如rbayesianoptimization库。以下是我的代码与rbayesianoptimization库。
cv_folds <- KFold(dataFTR$isPreIctalTrain, nfolds = 5, stratified = FALSE, seed = seedNum)
xgb_cv_bayes <- function(nround,max.depth, min_child_weight, subsample,eta,gamma,colsample_bytree,max_delta_step) {
param<-list(booster = "gbtree",
max_depth = max.depth,
min_child_weight = min_child_weight,
eta=eta,gamma=gamma,
subsample = subsample, colsample_bytree = colsample_bytree,
max_delta_step=max_delta_step,
lambda = 1, alpha = 0,
objective = "binary:logistic",
eval_metric = "auc")
cv <- xgb.cv(params = param, data = dtrain, folds = cv_folds,nrounds = 1000,early_stopping_rounds = 10, maximize = TRUE, verbose = verbose)
list(Score = cv$evaluation_log$test_auc_mean[cv$best_iteration],
Pred=cv$best_iteration)
# we don't need cross-validation prediction and we need the number of rounds.
# a workaround is to pass the number of rounds(best_iteration) to the Pred, which is a default parameter in the rbayesianoptimization library.
}
OPT_Res <- BayesianOptimization(xgb_cv_bayes,
bounds = list(max.depth =c(3L, 10L),min_child_weight = c(1L, 40L),
subsample = c(0.6, 0.9),
eta=c(0.01,0.3),gamma = c(0.0, 0.2),
colsample_bytree=c(0.5,0.8),max_delta_step=c(1L,10L)),
init_grid_dt = NULL, init_points = 10, n_iter = 10,
acq = "ucb", kappa = 2.576, eps = 0.0,
verbose = verbose)
best_param <- list(
booster = "gbtree",
eval.metric = "auc",
objective = "binary:logistic",
max_depth = OPT_Res$Best_Par["max.depth"],
eta = OPT_Res$Best_Par["eta"],
gamma = OPT_Res$Best_Par["gamma"],
subsample = OPT_Res$Best_Par["subsample"],
colsample_bytree = OPT_Res$Best_Par["colsample_bytree"],
min_child_weight = OPT_Res$Best_Par["min_child_weight"],
max_delta_step = OPT_Res$Best_Par["max_delta_step"])
# number of rounds should be tuned using CV
#https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/beginners-tutorial-on-xgboost-parameter-tuning-r/tutorial/
# However, nrounds can not be directly derivied from the bayesianoptimization function
# Here, OPT_Res$Pred, which was supposed to be used for cross-validation, is used to record the number of rounds
nrounds=OPT_Res$Pred[[which.max(OPT_Res$History$Value)]]
xgb_model <- xgb.train (params = best_param, data = dtrain, nrounds = nrounds)
mdcv <- xgb.cv(data=dtrain, params = param, nthread=6, nfold=cv.nfold, nrounds=cv.nround, verbose = T, early.stop.round=8, maximize=FALSE)，CV在测试日志损失增加了10多轮后仍未停止，对此有什么想法吗？谢谢！ - snowneji