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캐럿 패키지에서 rfe 함수를 사용하려고하지만 ROC 메트릭을 사용하는 gbm 모델에서 작동하지 않습니다. 나는이 코드 조각으로 종료 한캐럿 ref + gbm + ROC
Feature Selection in caret rfe + sum with ROC
http://www.cybaea.net/Blogs/Feature-selection-Using-the-caret-package.html
:
나는 거기에 몇 가지 통찰력을 발견gbmFuncs <- treebagFuncs
gbmFuncs$fit <- function (x, y, first, last, ...) {
library("gbm")
n.levels <- length(unique(y))
if (n.levels == 2) {
distribution = "bernoulli"
} else {
distribution = "gaussian"
}
gbm.fit(x, y, distribution = distribution, ...)
}
gbmFuncs$pred <- function (object, x) {
n.trees <- suppressWarnings(gbm.perf(object,
plot.it = FALSE,
method = "OOB"))
if (n.trees <= 0) n.trees <- object$n.trees
predict(object, x, n.trees = n.trees, type = "link")
}
control <- rfeControl(functions = gbmFuncs, method = "cv", verbose = TRUE, returnResamp="final",
number = 5)
trainctrl <- trainControl(classProbs= TRUE,
summaryFunction = twoClassSummary)
gbmFit_bernoulli_sel <- rfe(data_model[x, -as.numeric(y)+2,
sizes=c(10, 15, 20, 30, 40, 50), rfeControl = control, verbose = FALSE,
interaction.depth = 14, n.trees = 10000, shrinkage = .01, metric="ROC",
trControl = trainctrl)
그러나 나는이 오류를 얻을 :
을Error in { :
task 1 failed - "argument inutilisé (trControl = list(method = "boot", number = 25, repeats = 25, p = 0.75, initialWindow = NULL, horizon = 1, fixedWindow = TRUE, verboseIter = FALSE, returnData = TRUE, returnResamp = "final", savePredictions = FALSE, classProbs = TRUE, summaryFunction = function (data, lev = NULL, model = NULL)
{
require(pROC)
if (!all(levels(data[, "pred"]) == levels(data[, "obs"]))) stop("levels of observed and predicted data do not match")
rocObject <- try(pROC::roc(data$obs, data[, lev[1]]), silent = TRUE)
rocAUC <- if (class(rocObject)[1] == "try-error") NA else rocObject$auc
out <- c(rocAUC, sensitivity(data[, "pred"], data[, "obs"], lev[1]), specificity(data[, "pred"], data[, "obs"], lev[2]))
names(out) <- c("ROC", "Sens", "Spec")
out
편집
일 :
caretFuncs$summary <- twoClassSummary
controlrfe <- rfeControl(functions = caretFuncs, method = "cv", number = 3, verbose = TRUE)
gbmGrid <- expand.grid(interaction.depth = 5, n.trees = 1000, shrinkage = .01)
confroltrain <- trainControl(method = "none", classProbs=T, summaryFunction = twoClassSummary, verbose = TRUE)
gbmFit_bernoulli_sel <- rfe(data_model[,-ncol(data_model)], data_model[,ncol(data_model)],
sizes=c(10,15), rfeControl = controlrfe, metric="ROC",
trControl = confroltrain, tuneGrid=gbmGrid, method="gbm")
은 내가 gbmFuncs을 사용할 때, 나는 몇 가지 문제가 있었기 때문에 gbm.fit는 숫자 목표 변수를 필요가 분명히 있기 때문에 기차 기능을 사용했지만, ROC 메트릭 평가에는 요인이 필요합니다.
도움 주셔서 감사합니다.