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我对中的权重参数有疑问gbm。似乎将权重设置为 NULL 并使其丢失会产生不同的结果。如果您考虑文档中的第一个示例gbm：

library(gbm)
set.seed(1986)
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
X5 <- factor(sample(letters[1:3],N,replace=TRUE))
X6 <- 3*runif(N) 
mu <- c(-1,0,1,2)[as.numeric(X3)]

SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)

# introduce some missing values
X1[sample(1:N,size=500)] <- NA
X4[sample(1:N,size=300)] <- NA

data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)

# fit initial model with weight missing
gbm1 <-
  gbm(Y~X1+X2+X3+X4+X5+X6,         # formula
      data=data,                   # dataset
      var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
      # +1: monotone increase,
      #  0: no monotone restrictions
      distribution="gaussian",     # see the help for other choices
      n.trees=1000,                # number of trees
      shrinkage=0.05,              # shrinkage or learning rate,
      # 0.001 to 0.1 usually work
      interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
      bag.fraction = 0.5,          # subsampling fraction, 0.5 is probably best
      train.fraction = 0.5,        # fraction of data for training,
      # first train.fraction*N used for training
      n.minobsinnode = 10,         # minimum total weight needed in each node
      cv.folds = 3,                # do 3-fold cross-validation
      keep.data=TRUE,              # keep a copy of the dataset with the object
      verbose=FALSE,               # don't print out progress
      n.cores=1) 
# fit model with weight set to null
gbm2 <-
  gbm(Y~X1+X2+X3+X4+X5+X6,         # formula
      weights=NULL,                # extra weight parameter
      data=data,                   # dataset
      var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
      # +1: monotone increase,
      #  0: no monotone restrictions
      distribution="gaussian",     # see the help for other choices
      n.trees=1000,                # number of trees
      shrinkage=0.05,              # shrinkage or learning rate,
      # 0.001 to 0.1 usually work
      interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
      bag.fraction = 0.5,          # subsampling fraction, 0.5 is probably best
      train.fraction = 0.5,        # fraction of data for training,
      # first train.fraction*N used for training
      n.minobsinnode = 10,         # minimum total weight needed in each node
      cv.folds = 3,                # do 3-fold cross-validation
      keep.data=TRUE,              # keep a copy of the dataset with the object
      verbose=FALSE,               # don't print out progress
      n.cores=1)
summary(gbm1,1)
summary(gbm2,1)

这两个模型的结果是不同的，但我不清楚为什么。NULL 和缺失权重之间的含义有什么区别gbm？

亲切的问候

4

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