查看您的預測爲隨機森林R中的實際值

Question

所以我有一個數據集爲162 X 151： -

RT (seconds) 76_TI2 114_DECC 120_Lop 212_PCD 
38 4.086 1.2 2.322 0 
40 2.732 0.815 1.837 1.113 
41 4.049 1.153 2.117 2.354 
41 4.049 1.153 2.117 3.838 
42 4.56 1.224 2.128 2.38 
42 2.96 0.909 1.686 0.972 
42 3.237 0.96 1.922 1.202 
44 2.989 0.8 1.761 2.034 

，我想建立基於10倍交叉驗證它的隨機森林模型，然後查看每個摺疊的預測值和實際值。我正在使用randomForest包。我所做的： -

> set.seed(1500) 
> model <- rfcv(x,y, cv.fold=10) 

，但我沒能找到簡單地查看每個倍和與之相對應的實際值獲得的所有的預測值的方法。我如何去做這件事？

由於

Answer 1

爲交叉驗證獲得

預測值存儲在model$predicted[[1]]，觀測值是y。如果您想分別查看每個摺疊的預測值，則需要獲取有關摺疊分割的信息。要做到這一點，您可以：

1）分手動摺疊和自己

2）使用caret包

3）稍微修改cvrf輸出這些信息做交叉驗證 - 添加idx到輸出名單

rfcv2 <- function (trainx, trainy, cv.fold = 5, scale = "log", step = 0.5, 
      mtry = function(p) max(1, floor(sqrt(p))), recursive = FALSE, 
      ...) 
{ 
    classRF <- is.factor(trainy) 
    n <- nrow(trainx) 
    p <- ncol(trainx) 
    if (scale == "log") { 
    k <- floor(log(p, base = 1/step)) 
    n.var <- round(p * step^(0:(k - 1))) 
    same <- diff(n.var) == 0 
    if (any(same)) 
     n.var <- n.var[-which(same)] 
    if (!1 %in% n.var) 
     n.var <- c(n.var, 1) 
    } 
    else { 
    n.var <- seq(from = p, to = 1, by = step) 
    } 
    k <- length(n.var) 
    cv.pred <- vector(k, mode = "list") 
    for (i in 1:k) cv.pred[[i]] <- trainy 
    if (classRF) { 
    f <- trainy 
    } 
    else { 
    f <- factor(rep(1:5, length = length(trainy))[order(order(trainy))]) 
    } 
    nlvl <- table(f) 
    idx <- numeric(n) 
    for (i in 1:length(nlvl)) { 
    idx[which(f == levels(f)[i])] <- sample(rep(1:cv.fold, 
               length = nlvl[i])) 
    } 
    for (i in 1:cv.fold) { 
    all.rf <- randomForest(trainx[idx != i, , drop = FALSE], 
          trainy[idx != i], trainx[idx == i, , drop = FALSE], 
          trainy[idx == i], mtry = mtry(p), importance = TRUE, 
          ...) 
    cv.pred[[1]][idx == i] <- all.rf$test$predicted 
    impvar <- (1:p)[order(all.rf$importance[, 1], decreasing = TRUE)] 
    for (j in 2:k) { 
     imp.idx <- impvar[1:n.var[j]] 
     sub.rf <- randomForest(trainx[idx != i, imp.idx, 
            drop = FALSE], trainy[idx != i], trainx[idx == 
                       i, imp.idx, drop = FALSE], trainy[idx == i], 
          mtry = mtry(n.var[j]), importance = recursive, 
          ...) 
     cv.pred[[j]][idx == i] <- sub.rf$test$predicted 
     if (recursive) { 
     impvar <- (1:length(imp.idx))[order(sub.rf$importance[, 
                   1], decreasing = TRUE)] 
     } 
     NULL 
    } 
    NULL 
    } 
    if (classRF) { 
    error.cv <- sapply(cv.pred, function(x) mean(trainy != 
                x)) 
    } 
    else { 
    error.cv <- sapply(cv.pred, function(x) mean((trainy - 
                x)^2)) 
    } 
    names(error.cv) <- names(cv.pred) <- n.var 
    list(n.var = n.var, error.cv = error.cv, predicted = cv.pred, idx = idx) 
} 

現在，您可以撥打

model <- rfcv2(x,y, cv.fold=10) 
model$idx # returns the folds split. 

請注意，cvrf函數並非爲純粹的交叉驗證而設計，而是用於變量選擇。因此，您執行了大量冗餘計算。