Question

我使用xgboost R包执行多类分类任务。这是我创建的一段代码来说明问题（输入和输出是随机生成的，因此结果当然没有意义，它是我为了解决方案而学习如何处理包的事情）：

require(xgboost)
# First of all I set some parameters
featureNumber = 5
num_class = 4
obs = 1000

# I declare  a function that I will use to generate my categorical labels
generateLabels <- function(x,num_class){
  label <- 0
  if(runif(1,min=0,max =1) <0.1){
      label <- 0
  }else{
      label <- which.max(x) -1
      foo <- runif(1,min=0,max =1)
      if(foo > 0.9){label <- label + 1}
      if(foo < 0.1){label <- label - 1}
  }
  return(max(min(label,num_class-1),0))
}

# I generate a random train set and his labels
features <- matrix(runif(featureNumber*obs, 1, 10), ncol = featureNumber)
labels <- apply(features, 1, generateLabels,num_class = num_class) 
dTrain <- xgb.DMatrix(data = features, label = labels)

# I generate a random test set and his labels
testObs = floor(obs*0.25)
featuresTest <- matrix(runif(featureNumber*testObs, 1, 10), ncol = featureNumber)
labelsTest <- apply(featuresTest, 1, generateLabels, num_class = num_class) 
dTest <- xgb.DMatrix(data = featuresTest, label = labelsTest)

# I train the 
xgbm   <- xgb.train(data = dTrain, 
                  nrounds = 10,
                  objective = "multi:softprob", 
                  eval_metric = "mlogloss", 
                  watchlist = list(train=dTrain, eval=dTest),                          
                  num_class = featureNumber)

这可以按预期工作并产生预期的结果，这里有几行：

[0] train-mlogloss:1.221495 eval-mlogloss:1.292785
[1] train-mlogloss:0.999905 eval-mlogloss:1.121077
[2] train-mlogloss:0.846809 eval-mlogloss:1.014519
[3] train-mlogloss:0.735182 eval-mlogloss:0.942461
[4] train-mlogloss:0.650207 eval-mlogloss:0.891341
[5] train-mlogloss:0.580136 eval-mlogloss:0.851774
[6] train-mlogloss:0.524390 eval-mlogloss:0.827973
[7] train-mlogloss:0.475884 eval-mlogloss:0.815081
[8] train-mlogloss:0.435342 eval-mlogloss:0.799799
[9] train-mlogloss:0.402307 eval-mlogloss:0.789209

我无法实现的是存储这些值以便以后使用它们。是否有可能做到这一点？调整参数非常有用。

P.S。我知道我可以使用包中包含的交叉验证方法xgb.cv来获得类似的结果;但我宁愿使用这种方法来控制所发生的事情，因为这些指标都是计算出来的，在我看来，浪费了计算能力，除了在屏幕上阅读之外不能使用它们。

Answer 1

您可以使用xbgm$bestScore和xbgm$bestInd

访问最佳回合参数

访问xgboost r包中的监视列表历史记录

1 个答案: