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我正在使用 xgboost R 包来执行多类分类任务。这是我为说明问题而创建的一段代码（输入和输出是随机生成的，因此结果当然没有意义，这是我所做的只是为了玩耍并学习如何处理包）：

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供以后使用。是否有可能做到这一点？调整参数会很有帮助。

PS 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更好地控制发生的事情，而且，由于这些指标是计算出来的，在我看来，除了在屏幕上阅读之外，没有可能使用它们，这似乎是一种计算能力的浪费。

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1 回答 1

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xbgm$bestScore您可以使用和访问最佳回合的参数xbgm$bestInd

于 2016-04-27T12:13:25.307 回答