隨機均勻森林迴歸

的計算AUC我有一個數據幀df隨機均勻森林迴歸

df<-structure(list(ID = structure(c(1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 
3L, 3L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L), .Label = c("AU-Tum", 
"AU-Wac", "BE-Bra", "BE-Jal", "BR-Cax", "BR-Sa3", "CA-Ca1", "CA-Ca2", 
"CA-Ca3", "CA-Gro", "Ca-Man", "CA-NS1", "CA-NS2", "CA-NS3", "CA-NS4", 
"CA-NS5", "CA-NS6", "CA-NS7", "CA-Oas", "CA-Obs", "CA-Ojp", "CA-Qcu", 
"CA-Qfo", "CA-SF1", "CA-SF2", "CA-SF3", "CA-SJ1", "CA-SJ2", "CA-SJ3", 
"CA-TP1", "CA-TP2", "CA-TP4", "CN-Cha", "CN-Ku1", "CZ-Bk1", "De-Bay", 
"DE-Har", "DE-Tha", "DE-Wet", "DK-Sor", "FI-Hyy", "FI-Sod", "FR-Hes", 
"FR-Pue", "GF-Guy", "ID-Pag", "IL-Yat", "IT-Col", "IT-Lav", "IT-Non", 
"IT-Ro1", "IT-Ro2", "IT-Sro", "JP-Tak", "JP-Tef", "JP-Tom", "NL-Loo", 
"PT-Esp", "RU-Fyo", "SE-Abi", "SE-Fla", "SE-Nor", "SE-Sk1", "SE-Sk2", 
"SE-St1", "UK-Gri", "UK-Ham", "US-Blo", "US-Bn1", "US-Bn2", "Us-Bn3", 
"US-Dk3", "US-Fmf", "US-Fwf", "US-Ha1", "US-Ha2", "US-Ho1", "US-Ho2", 
"US-Lph", "US-Me1", "US-Me3", "US-Nc2", "US-NR1", "US-Oho", "US-Sp1", 
"US-Sp2", "US-Sp3", "US-Syv", "US-Umb", "US-Wcr", "US-Wi0", "US-Wi1", 
"US-Wi2", "US-Wi4", "US-Wi8", "VU-Coc", "Austin", "Caxiuana", 
"Mae Klong", "Niwot Ridge", "Sky Oaks old", "Sky Oaks young", 
"Sodankylä", "Tomakomai", "Yenisey Abies", "Yenisey Betula", 
"Yenisey Mixed"), class = "factor"), P = c(1241.59999960661, 
1282.40000277758, 0, 895, 0, 960.399999260902, 988.300011262298, 
778.211069688201, 0, 676.725008800626, 1750.51986303926, 1614.11541634798, 
951.847023338079, 1119.3682884872, 1112.38984390156, 1270.65773075982, 
1234.72262170166, 1338.46096616983, 1136.69287367165, 1265.46480803983 
), Te = c(9.20406423444821, 9.58323018294185, NaN, 12.1362462834136, 
NaN, 10.6474634506736, 10.2948508957069, 11.3363996068107, NaN, 
11.9457507949986, 9.10006221322572, 7.65505467142961, 8.21480062559674, 
8.09251754304318, 8.466220758789, 8.48094407814006, 8.77304120569444, 
8.31727518543397, 8.80921738865237, 9.04091478341757), Y = c(2172.34112930298, 
2479.44521586597, 1027.63470042497, 2342.35314202309, 868.4010617733, 
1157.13594430499, 1118.60130960867, 1100.47051284742, 1072.57190890331, 
1228.25697739795, 2268.14043972082, 2147.62290922552, 2269.1387550775, 
2247.31983098201, 1903.39138268307, 2174.78291538358, 2359.51909126411, 
2488.39004804939, 461.398994384333, 567.150629704352)), .Names = c("ID", 
"P", "Te", "Y"), row.names = 307:326, class = "data.frame")

我已經在LOO編號CV模式實現了一個隨機均勻森林的過程。

library (randomUniformForest) 
df$prediction <- NA 
for(id in unique(df$ID)){ 
    train.df <- df[df$ID != id,] 
    test.df <- df[df$ID == id,c("P", "Te")] 
    svmFit <- randomUniformForest(Y ~ P+Te, 
           data = train.df, 
           importance = T, 
           ntree = 100) 
    ruf_pred = predict(object = svmFit, X = test.df) 
    df$prediction[df$ID == id] <- ruf_pred 
}

我想計算我的模型的AUC值。不知何故，它不會顯示在下面的命令行中。

statsModel = model.stats(df$prediction, df$Y, regression = TRUE)

有誰知道我可以如何計算我的模型的AUC值？

來源

2015-11-19 SimonB

你可能想看看'pROC'和'ROCR'軟件包。 – RHertel

我試過這個'auc（df $ predict，df $ Y）'''pROC'包但值是1.沒有意義。 – SimonB

我所知道的是ROC曲線的AUC常用於logit模型等，因此存在二元結果。由於您的預測值超出了0-1的範圍，因爲您沒有使用二元結果，所以您的AUC值爲1.這是我最好的猜測。另見討論[這裏]（http://stackoverflow.com/questions/10725396/r-fast-auc-function-for-non-binary-dependent-variable） – BlankUsername

這是改編自ROCR包的文檔的例子：

library(ROCR) 
data(ROCR.simple) 
pred <- prediction(ROCR.simple$predictions,ROCR.simple$labels) 
cat("AUC=",attributes(performance(pred, 'auc'))$y.values[[1]],"\n") 
#AUC= 0.8341875

也許你可以用這個你的問題。

來源

2015-11-19 17:22:12 RHertel

我看到了，但沒有真正得到如何'標籤「被定義。任何線索？ – SimonB

這是一個二元分類問題：1或0.這是使用AUC時的典型情況。這是衡量模型識別真實肯定而不是誤報的常用指標。 – RHertel

隨機均勻森林迴歸

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