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插入符号:带tuneLength和rerank的RFE_R_Machine Learning_R Caret - Fatal编程技术网

插入符号:带tuneLength和rerank的RFE

插入符号:带tuneLength和rerank的RFE,r,machine-learning,r-caret,R,Machine Learning,R Caret,当使用设置了tuneLength且rerank=TRUE的rfe时,在重新计算要素子集的排名时,tuneLength中的参数是否优化? 以下是我正在进行的分析示例: library("caret") set.seed(342) train <- as.data.frame ( matrix( rnorm(1e4) , 100, 100 ) ) ctrl <- rfeControl(functions = caretFuncs, rera

当使用设置了tuneLength且rerank=TRUE的rfe时,在重新计算要素子集的排名时,tuneLength中的参数是否优化? 以下是我正在进行的分析示例:

library("caret")
set.seed(342)
train <- as.data.frame ( matrix( rnorm(1e4) , 100, 100 ) )

ctrl <- rfeControl(functions = caretFuncs,        
               rerank = TRUE,
               method = "repeatedcv",
               number=2, 
               repeats=1,
               verbose =TRUE
)

pls.fit.rfe <- rfe(V1 ~ .,
               data = train,   
               method = "pls",                    
               sizes =  c(2,5),
               tuneLength = 5, 
               rfeControl = ctrl
)

重新排序发生在每个PLS拟合之后。我不认为排名对模型调整有任何直接影响

麦克斯:是的,我读过。但是排名将取决于我的示例中1:tuneLength ncomp中参数的选择。那么,在计算重库时,是否存在交叉验证的NCOMP优化?