r - 控制台和 Rmarkdown 的准确度结果不同

Question

我有多个准确度不同的分类机器学习模型。当我在控制台中运行我的 xgBOOST(使用 library(caret))时，我得到了 0.7586 的准确度。但是当我编织我的 Rmarkdown 时，相同模型的准确率是 0.8621。我不知道为什么这是不同的。
我按照此链接的建议进行操作，但没有任何效果:https://community.rstudio.com/t/console-and-rmd-output-differ-same-program-used-but-the-calculation-gives-a-different-result/67873/3
我也遵循了问题的建议，但没有任何效果:Statistics Result in R Markdown is different from the Knit Output (All Format: Word, HTML, PDF)
最后我尝试了这个，但也没有任何效果:sample function gives different result in console and in knitted document when seed is set
这是我在控制台和 Rmarkdown 中运行的代码，但准确度不同:

    # Data
    data <- data[!is.na(data$var1),]

# Change levels of var1
levels(data$var1)=c("No","Yes")

#Data Preparation and Preprocessing
# Create the training and test datasets
set.seed(100)

# Step 1: Get row numbers for the training data
trainRowNumbers <- createDataPartition(data$var1, p=0.8, list=FALSE)

# Step 2: Create the training  dataset
trainset <- data[trainRowNumbers,]

# Step 3: Create the test dataset
testset <- data[-trainRowNumbers,]

# Store Y for later use.
y = trainset$var1

# Create the knn imputation model on the training data
preProcess_missingdata_model <- preProcess(as.data.frame(trainset), method= c("knnImpute"))
preProcess_missingdata_model

# Create the knn imputation model on the testset data
preProcess_missingdata_model_test <- preProcess(as.data.frame(testset), method = c("knnImpute"))
preProcess_missingdata_model_test

# Use the imputation model to predict the values of missing data points
library(RANN)  # required for knnInpute
trainset <- predict(preProcess_missingdata_model, newdata = trainset)
anyNA(trainset)

# Use the imputation model to predict the values of missing data points
library(RANN)  # required for knnInpute
testset <- predict(preProcess_missingdata_model_test, newdata = testset)
anyNA(testset)

# Append the Y variable
trainset$var1 <- y

# Run algorithms using 5-fold cross validation
control <- trainControl(method="cv", 
                        number=5, 
                        repeats = 5, 
                        savePredictions = "final", 
                        search = "grid", 
                        classProbs = TRUE)
metric <- "Accuracy"

# Make Valid Column Names 
colnames(trainset) <- make.names(colnames(trainset))
colnames(testset) <- make.names(colnames(testset))

# xgBOOST
set.seed(7)
fit.xgbDART <- train(var1~., data = trainset, method = "xgbTree", metric = metric, trControl = control, verbose = FALSE, tuneLength = 7, nthread = 1)

# estimate skill of xgBOOST on the testset dataset
predictions <- predict(fit.xgbDART, testset)
cm <- caret::confusionMatrix(predictions, testset$var1, mode='everything')
cm

My RNGKind is:

RNGkind()
[1] "L'Ecuyer-CMRG" "Inversion"     "Rejection" 

Answer 1

总是添加功能:

set.seed(544) 

此函数设置用于生成随机数序列的起始编号 - 如果每次运行相同的过程时都使用相同的种子开始，它可以确保获得相同的结果。例如，如果我在设置种子后立即使用 sample() 函数，我将始终获得相同的样本。