预测测试数据的结果从一个随机森林对象

我有一个数据帧df预测测试数据的结果从一个随机森林对象

dput(df) 
    structure(list(ID = c(4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 
6, 6, 6, 6, 8, 8, 8, 9, 9), Y = c(2268.14043972082, 2147.62290922552, 
2269.1387550775, 2247.31983098201, 1903.39138268307, 2174.78291538358, 
2359.51909126411, 2488.39004804939, 212.851575751527, 461.398994384333, 
567.150629704352, 781.775113821961, 918.303706148872, 1107.37695799186, 
1160.80594193377, 1412.61328924168, 1689.48879626486, 685.154353165934, 
574.088067465695, 650.30821636616, 494.185166497016, 436.312162090908 
), P = c(1750.51986303926, 1614.11541634798, 951.847023338079, 
1119.3682884872, 1112.38984390156, 1270.65773075982, 1234.72262170166, 
1338.46096616983, 1198.95775346458, 1136.69287367165, 1265.46480803983, 
1364.70149818063, 1112.37006707489, 1346.49240261316, 1740.56677791104, 
1410.99217295647, 1693.18871380948, 275.447173420805, 396.449789014179, 
251.609239829704, 215.432550271042, 55.5336257666349), A = c(49, 
50, 51, 52, 53, 54, 55, 56, 1, 2, 3, 4, 5, 14, 15, 16, 17, 163, 
164, 165, 153, 154), TA = c(9.10006221322572, 7.65505467142961, 
8.21480062559674, 8.09251754304318, 8.466220758789, 8.48094407814006, 
8.77304120569444, 8.31727518543397, 8.14410265791868, 8.80921738865237, 
9.04091478341757, 9.66233618146246, 8.77015716015164, 9.46037931956657, 
9.59702379240667, 10.1739258740118, 9.39524442215692, -0.00568604734662462, 
-2.12940164413048, -0.428603434930109, 1.52337963973006, -1.04714984064565 
), TS = c(9.6499861763085, 7.00622420539595, 7.73511170298675, 
7.68006974050443, 8.07442411510912, 8.27687965909096, 8.76025039592727, 
8.3345638889156, 9.23658956753677, 8.98160722605782, 8.98234210211611, 
9.57066566368204, 8.74444401914267, 8.98719629775988, 9.18169205278566, 
9.98225438314085, 9.56196773059615, 5.47788158053928, 2.58106090926808, 
3.22420704848299, 1.36953555753786, 0.241334267522977), R = c(11.6679680423377, 
11.0166459173372, 11.1851268491296, 10.7404563561694, 12.1054055597684, 
10.9551321815546, 11.1975918244469, 10.7242192465965, 10.1661703705992, 
11.4840412725324, 11.1248456370953, 11.2529612597628, 10.7694642397996, 
12.3300887767583, 12.0478558531771, 12.3212362249214, 11.5650773932264, 
9.56070414783612, 9.61762902218185, 10.2076240621201, 11.8234628013552, 
10.9184029778985)), .Names = c("ID", "Y", "P", "A", "TA", "TS", 
"R"), na.action = structure(77:78, .Names = c("77", "78"), class = "omit"), row.names = c(NA, 
22L), class = "data.frame")

我已经在这个数据集运行随机森林与留一个主题交叉验证。见下文。

library (caret) 
library(randomForest) 
    # Create training dataset 
    subs <- unique(df$ID) 
    train<- vector(mode = "list", length = length(subs)) 
    test<- vector(mode = "list", length = length(subs)) 

# Run a RandomForest with leave one out ID CV 
for(i in seq_along(subs)) 
train[[i]] <- which(df$ID != subs[i]) 
names(train) <- paste0("ID", subs) 
rfFit <- train(Y~ P + TA + TS + R + A, 
       data =df, 
       method = "rf", 
       ntree = 100, 
       prox=TRUE, allowParallel=TRUE, 
       importance = TRUE, 
       trControl = trainControl(method = "cv", 
             index = train))

我现在要预测上测试数据集的随机森林对象的结果（见下文）

# Test dataset 
    for(i in seq_along(subs)) 
    test[[i]] <- subset(df[df$ID == subs[i],])

然而，我无法实现它。有人可以帮我解决吗？

来源

2015-10-05 SimonB

井测试数据集是前人的精力对象的列表，每个对象都是具有相同ID数据框的行。我有错误的方法来创建我的测试数据集？ – SimonB

您可以添加在循环的预测如下图所示：

results <- list() 
for(i in seq_along(subs)) { 
     test[[i]] <- subset(df[df$ID == subs[i],]) 
     results[[i]] <- predict(rfFit, test[[i]]) 
} 

print(results) 
[[1]] 
[1] 1913.784 2194.669 1984.460 1951.577 1907.284 2176.945 2025.345 2221.992 

[[2]] 
[1] 664.2550 746.0124 851.9516 960.3064 1037.9849 

[[3]] 
[1] 1132.243 1169.022 1292.756 1523.960 

[[4]] 
[1] 697.4928 603.5060 610.2461 

[[5]] 
[1] 667.2926 601.2800

来源

2015-10-05 17:46:57 phiver

非常感谢。我的数据集中也有一些错误，因为我的观察和预测之间几乎没有相关性！ – SimonB

预测测试数据的结果从一个随机森林对象

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