R 插入符 : Tuning GLM boost prune parameter

Question

我正在尝试调整 GLM boost 模型的参数。根据Caret package documentation对于这个模型，有 2 个参数可以调整，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. 

修剪参数保持不变(调整参数“修剪”的值保持恒定为 yes)，尽管 glmBoostGrid 还包含 prune == no 。我查看了mboost包文档中的boost_control方法，只有mstop参数可以访问，那么如何修剪prune 参数可以使用 train 方法的 tuneGrid 参数进行调整吗？

Answer 1

差异在于 glmboost 调用的这一部分:

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，在插入符号中使用 glmboost 运行各种测试时，我怀疑它的行为是否符合预期。我在 github 上创建了一个问题来看看我的怀疑是否正确。如果开发人员提供更多信息，我将编辑我的答案。