gbm()分类-错误

Question

cancer <- read.csv('breast-cancer-wisconsin.data', header = FALSE, na.strings="?")
cancer <- cancer[complete.cases(cancer),]
names(cancer)[11] <- "class"
cancer[, 11] <- factor(cancer[, 11], labels = c("benign", "malignant"))
library(gbm)

• 数据
• 数据描述

首先，我使用complete.cases删除'NA‘值，并将第11列"class“作为因子。我想使用"class“作为响应变量和其他列(除了第一个列)作为预测变量。

在我第一次尝试时，我输入了：

boost.cancer <- gbm(class ~ .-V1, data = cancer, distribution = "bernoulli") 

Error in gbm.fit(x, y, offset = offset, distribution = distribution, w = w,  : 
Bernoulli requires the response to be in {0,1}

然后，我用班级的对比而不是课堂。

boost.cancer <- gbm(contrasts(class) ~ .-V1, distribution = "bernoulli", data = cancer)

Error in model.frame.default(formula = contrasts(class) ~ . - V1, data = cancer,  : 
variable lengths differ (found for 'V1')

如何纠正这些错误？我肯定我的方法有问题。

Answer 1

正如错误所述，您的响应不在0,1中。您可以这样做，而不是创建以下因素：

> cancer$class <- (cancer$class -2)/2

> boost.cancer <- gbm(class ~ .-V1, data = cancer, distribution = "bernoulli")
> boost.cancer
gbm(formula = class ~ . - V1, distribution = "bernoulli", data = cancer)
A gradient boosted model with bernoulli loss function.
100 iterations were performed.
There were 9 predictors of which 4 had non-zero influence.

Answer 2

您还可以使用：

boost.cancer <- gbm((unclass(class(Class)-1)-1)~-V1，数据=癌症，distribution = "bernoulli")摘要(boost.cancer)

在“预测”函数和精确确定混淆矩阵的同时，做类似的事情。