机器算法验证 - 如何查看 GBM 包树？ - 吾爱随笔录

我正在尝试查看 R 中增强树的 GBM 包的输出。下面我正在拟合一棵没有任何采样的树，以便将树与完整的数据集进行比较。首先，创建数据集：

  set.seed(1973)

############## CREATE DATA#############################################
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 model##############################################################
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",     # bernoulli, adaboost, gaussian,
                                 # poisson, coxph, and quantile available
    n.trees=1,                   # number of trees
    shrinkage=1,                 # shrinkage or learning rate,
                                 # 0.001 to 0.1 usually work
    interaction.depth=1,         # 1: additive model, 2: two-way interactions, etc.
    bag.fraction = 1,            # subsampling fraction, 0.5 is probably best
    train.fraction = 1,          # fraction of data for training,
                                 # first train.fraction*N used for training
    n.minobsinnode = 10,         # minimum total weight needed in each node
    keep.data=TRUE,              # keep a copy of the dataset with the object
    verbose=TRUE)                # print out progress

###########################################################################

接下来，看树。我认为这表明 X2 的分裂值为 1.5。然而，这表明有 522 个记录了一个方向，而 478 个记录了另一个。查看数据，此记录拆分与计数不对应。有什么见解吗？这是一个错误吗？

pretty.gbm.tree(gbm1,i.tree = 1)
length(d<-subset(data, data$X2>1.50,3)[,1])