文档指出，具有 distribution = "adaboost" 的 R gbm 可用于 0-1 分类问题。考虑以下代码片段：
gbm_algorithm <- gbm(y ~ ., data = train_dataset, distribution = "adaboost", n.trees = 5000)
gbm_predicted <- predict(gbm_algorithm, test_dataset, n.trees = 5000)
它可以在 predict.gbm 的文档中找到
返回预测向量。默认情况下，预测在 f(x) 的范围内。
然而，对于分布 =“adaboost”的情况，具体规模尚不清楚。
任何人都可以帮助解释 predict.gbm 返回值并提供转换为 0-1 输出的想法吗？
您也可以直接从predict.gbm函数中获取概率；
predict(gbm_algorithm, test_dataset, n.trees = 5000, type = 'response')
adaboost 方法给出了 logit 尺度的预测。您可以将其转换为 0-1 输出：
gbm_predicted<-plogis(2*gbm_predicted)
注意logis里面的2*
adaboost 链接功能在此处描述。此示例提供了计算的详细描述：
library(gbm);
set.seed(123);
n <- 1000;
sim.df <- data.frame(x.1 = sample(0:1, n, replace=TRUE),
x.2 = sample(0:1, n, replace=TRUE));
prob.array <- c(0.9, 0.7, 0.2, 0.8);
df$y <- rbinom(n, size = 1, prob=prob.array[1+sim.df$x.1+2*sim.df$x.2])
n.trees <- 10;
shrinkage <- 0.01;
gbmFit <- gbm(
formula = y~.,
distribution = "bernoulli",
data = sim.df,
n.trees = n.trees,
interaction.depth = 2,
n.minobsinnode = 2,
shrinkage = shrinkage,
bag.fraction = 0.5,
cv.folds = 0,
# verbose = FALSE
n.cores = 1
);
sim.df$logods <- predict(gbmFit, sim.df, n.trees = n.trees); #$
sim.df$prob <- predict(gbmFit, sim.df, n.trees = n.trees, type = 'response'); #$
sim.df$prob.2 <- plogis(predict(gbmFit, sim.df, n.trees = n.trees)); #$
sim.df$logloss <- sim.df$y*log(sim.df$prob) + (1-sim.df$y)*log(1-sim.df$prob); #$
gbmFit <- gbm(
formula = y~.,
distribution = "adaboost",
data = sim.df,
n.trees = n.trees,
interaction.depth = 2,
n.minobsinnode = 2,
shrinkage = shrinkage,
bag.fraction = 0.5,
cv.folds = 0,
# verbose = FALSE
n.cores = 1
);
sim.df$exp.scale <- predict(gbmFit, sim.df, n.trees = n.trees); #$
sim.df$ada.resp <- predict(gbmFit, sim.df, n.trees = n.trees, type = 'response'); #$
sim.df$ada.resp.2 <- plogis(2*predict(gbmFit, sim.df, n.trees = n.trees)); #$
sim.df$ada.error <- -exp(-sim.df$y * sim.df$exp.scale); #$
sim.df[1:20,]