我正在尝试调整GLM提升模型的参数。根据有关此模型的Caret软件包文档，有2个参数可以调整，即mstop和prune。
library(caret)
library(mlbench)
data(Sonar)
set.seed(25)
trainIndex = createDataPartition(Sonar$Class, p = 0.9, list = FALSE)
training = Sonar[ trainIndex,]
testing = Sonar[-trainIndex,]
### set training parameters
fitControl = trainControl(method = "repeatedcv",
number = 10,
repeats = 10,
## Estimate class probabilities
classProbs = TRUE,
## Evaluate a two-class performances
## (ROC, sensitivity, specificity) using the following function
summaryFunction = twoClassSummary)
### train the models
set.seed(69)
# Use the expand.grid to specify the search space
glmBoostGrid = expand.grid(mstop = c(50, 100, 150, 200, 250, 300),
prune = c('yes', 'no'))
glmBoostFit = train(Class ~ .,
data = training,
method = "glmboost",
trControl = fitControl,
tuneGrid = glmBoostGrid,
metric = 'ROC')
glmBoostFit
输出如下：
Boosted Generalized Linear Model
188 samples
60 predictors
2 classes: 'M', 'R'
No pre-processing
Resampling: Cross-Validated (10 fold, repeated 10 times)
Summary of sample sizes: 169, 169, 169, 169, 170, 169, ...
Resampling results across tuning parameters:
mstop ROC Sens Spec ROC SD Sens SD Spec SD
50 0.8261806 0.764 0.7598611 0.10208114 0.1311104 0.1539477
100 0.8265972 0.729 0.7625000 0.09459835 0.1391250 0.1385465
150 0.8282083 0.717 0.7726389 0.09570417 0.1418152 0.1382405
200 0.8307917 0.714 0.7769444 0.09484042 0.1439011 0.1452857
250 0.8306667 0.719 0.7756944 0.09452604 0.1436740 0.1535578
300 0.8278403 0.728 0.7722222 0.09794868 0.1425398 0.1576030
Tuning parameter 'prune' was held constant at a value of yes
ROC was used to select the optimal model using the largest value.
The final values used for the model were mstop = 200 and prune = yes.
Tuning parameter 'prune' was held constant at a value of yes尽管glmBoostGrid还包含，修剪参数保持常量（）prune == no。我查看了有关mboost该boost_control方法的软件包文档，并且只能mstop访问该参数，那么如何prune用tuneGrid该train方法的参数来调整该参数呢？
区别在于glmboost的调用部分：
if (param$prune == "yes") {
out <- if (is.factor(y))
out[mstop(AIC(out, "classical"))]
else out[mstop(AIC(out))]
}
区别在于aic的计算方式。但是在插入符中使用glmboost运行各种测试时，我怀疑它的行为是否符合预期。我在github中创建了一个问题，以查看我的怀疑是否正确。如果开发者有更多信息，我将编辑答案。
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