GBM R函数:为每个类分别获取变量重要性

Question

我在R(gbm包)中使用gbm函数来拟合用于多类分类的随机梯度增强模型.我只是试图分别为每个班级获得每个预测因子的重要性,就像哈斯蒂(Hastie)书(统计学习要素)(第382页)中的这张图片一样.

但是,该函数summary.gbm仅返回预测变量的总体重要性(它们对所有类的平均重要性).

有谁知道如何获得相对重要性值？

Answer 1

我认为简短的回答是,在第379页,Hastie提到他使用MART,它似乎只适用于Splus.

我同意gbm包似乎不允许看到单独的相对影响.如果你对mutliclass问题感兴趣,你可能会通过为每个类构建一个vs-all-gbm然后从每个模型中获取重要性度量来获得非常相似的东西.

所以说你的课程是a,b,c和d.您对其他模型进行建模,并从该模型中获得重要性.然后你模拟b与其余的模型,并从该模型中获得重要性.等等.

Answer 2

Hopefully this function helps you. For the example I used data from the ElemStatLearn package. The function figures out what the classes for a column are, splits the data into these classes, runs the gbm() function on each class and plots the bar plots for these models.

# install.packages("ElemStatLearn"); install.packages("gbm")
library(ElemStatLearn)
library(gbm)

set.seed(137531)

# formula: the formula to pass to gbm()
# data: the data set to use
# column: the class column to use
classPlots <- function (formula, data, column) {

    class_column <- as.character(data[,column])
    class_values <- names(table(class_column))
    class_indexes <- sapply(class_values, function(x) which(class_column == x))
    split_data <- lapply(class_indexes, function(x) marketing[x,])
    object <- lapply(split_data, function(x) gbm(formula, data = x))
    rel.inf <- lapply(object, function(x) summary.gbm(x, plotit=FALSE))

    nobjs <- length(class_values)
    for( i in 1:nobjs ) {
        tmp <- rel.inf[[i]]
        tmp.names <- row.names(tmp)
        tmp <- tmp$rel.inf
        names(tmp) <- tmp.names

        barplot(tmp, horiz=TRUE, col='red',
                xlab="Relative importance", main=paste0("Class = ", class_values[i]))
    }
    rel.inf
}

par(mfrow=c(1,2))
classPlots(Income ~ Marital + Age, data = marketing, column = 2)

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