在R（插入符号）中重新运行preProcess（），predict（）和train（）时，模型的精度不同

Question

下面的数据只是一个例子，它是对此或任何我感到困惑的数据的操作：

library(caret)
set.seed(3433)
data(AlzheimerDisease)
complete <- data.frame(diagnosis, predictors)
in_train <- createDataPartition(complete$diagnosis, p = 0.75)[[1]]
training <- complete[in_train,]
testing <- complete[-in_train,]
predIL <- grep("^IL", names(training))
smalltrain <- training[, c(1, predIL)]

fit_noPCA <- train(diagnosis ~ ., method = "glm", data = smalltrain)
pre_proc_obj <- preProcess(smalltrain[,-1], method = "pca", thresh = 0.8)
smalltrainsPCs <- predict(pre_proc_obj, smalltrain[,-1])
fit_PCA <- train(x = smalltrainsPCs, y = smalltrain$diagnosis, method = "glm")
fit_noPCA$results$Accuracy
fit_PCA$results$Accuracy

[运行此代码时，fit_noPCA的精度为0.689539，fit_PCA的精度为0.682951。但是当我重新运行代码的最后一部分时：

fit_noPCA <- train(diagnosis ~ ., method = "glm", data = smalltrain)
pre_proc_obj <- preProcess(smalltrain[,-1], method = "pca", thresh = 0.8)
smalltrainsPCs <- predict(pre_proc_obj, smalltrain[,-1])
fit_PCA <- train(x = smalltrainsPCs, y = smalltrain$diagnosis, method = "glm")
fit_noPCA$results$Accuracy
fit_PCA$results$Accuracy

然后，我每次重新运行这6行，都会得到不同的精度值。为什么会这样呢？是因为我没有重置种子吗？即使，此过程的固有随机性在哪里？

Answer 1

默认情况下，该模型是使用引导程序进行训练的，您可以在此处看到它：

> fit_noPCA
Generalized Linear Model 

251 samples
 12 predictor
  2 classes: 'Impaired', 'Control' 

No pre-processing
Resampling: Bootstrapped (25 reps) 
Summary of sample sizes: 251, 251, 251, 251, 251, 251, ... 
Resampling results:

  Accuracy   Kappa     
  0.6870006  0.04107016

因此，每个train的自举样本将有所不同，要返回相同的结果，可以在运行train之前设置种子：
set.seed(111)
fit_PCA <- train(x = smalltrainsPCs, y = smalltrain$diagnosis, method = "glm",trControl=trainControl(method="boot",number=100))
fit_PCA$results$Accuracy
[1] 0.6983512

set.seed(112)
fit_PCA <- train(x = smalltrainsPCs, y = smalltrain$diagnosis, method = "glm",trControl=trainControl(method="boot",number=100))
fit_PCA$results$Accuracy
[1] 0.6991537

set.seed(111)
fit_PCA <- train(x = smalltrainsPCs, y = smalltrain$diagnosis, method = "glm",trControl=trainControl(method="boot",number=100))
fit_PCA$results$Accuracy
[1] 0.6983512

或使用例如cv，您可以在index=中使用trainControl定义折页>