SVM圖書館e1071（預測功能總是返回一個類）中的R

編輯：我意識到，我可以使用dput()功能設定輸出SVM圖書館e1071（預測功能總是返回一個類）中的R

> dput(lc_train) 
structure(list(int_rate = structure(c(0.327601699000463, -1.90117708704328, 
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    -0.224228390326995, -0.277562815916827), "`scaled:center`" = 60.3450354609929, "`scaled:scale`" = 22.4995392137262), 
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"revol_util", "default"), class = c("data.table", "data.frame" 
), row.names = c(NA, -451L), .internal.selfref = <pointer: 0x101822d78>, index = integer(0))

我訓練的一部分，我從e1071運行svm包。

> svmfit = svm(default ~., data = lc_train, kernel = "linear", cost = 10, scale = FALSE) 
> summary(svmfit) 

Call: 
svm(formula = default ~ ., data = lc_train, kernel = "linear", cost = 10, scale = FALSE) 


Parameters: 
    SVM-Type: C-classification 
SVM-Kernel: linear 
     cost: 10 
     gamma: 0.3333333 

Number of Support Vectors: 1941 

(996 945) 


Number of Classes: 2 

Levels: 
-1 1 



> 
> ypred = predict(svmfit, lc_train) 
> unique(ypred) 
[1] -1 
Levels: -1 1

正如你可以從svmfit訓練數據彙總看出，SVM實際上並分成兩個班。看起來在一個類中有996個支持向量，在另一個類中有945個支持向量。

但是，當我在相同的訓練數據集lc_train上使用predict函數時，它告訴我所有的預測都是一個類（-1）。

我真的很困惑，因爲summary(svmfit)清楚地顯示了兩個類，我在每種情況下都使用相同的訓練數據集。

我正在使用數據表，特別是lc_train是一個數據表。我不確定這是否會搞砸了。

謝謝！

來源

2016-02-04 Vincent

我不能與虛擬數據重現您的問題。因此我懷疑它可能與數據的拓撲結構有關，儘管我無法真正想到可能導致這種奇怪行爲的事情。您是否很樂意給我們簡要描述您使用的數據？（維數，原始不平衡等） – Vongo

如果更改內核，是否仍然存在相同的問題？ – Vongo

我在更改內核時仍然遇到同樣的問題。另外，我意識到我可以使用dput（）來導出一部分數據集。所以我適當地編輯了我原來的帖子！ – Vincent

這裏是再現您的問題代碼：

library("data.table") 
library("e1071") 

set.seed(10) 

lc_train <- data.table(default = as.factor(sample(c(-1, 1), size = 200, replace = TRUE)), x1=rnorm(100), x2=rnorm(100)) 
svmfit <- svm(default ~., data = lc_train, kernel = "linear", cost = 10, scale = FALSE) 
summary(svmfit) 
ypred <- predict(svmfit, lc_train) 
table(ypred)

摘要顯示，你在你的數據有兩個類，這是真的。但svm僅預測一個類。這可能是因爲數據而發生的，您可能想要更改模型參數，如@Vongo建議的（kernel = "radial"）。

（一旦你改變隨機種子100 set.seed(100)在上面的代碼中，你會看到在預測兩班）。

來源

2016-02-04 11:23:01

我發現如此奇怪，因爲在上述的例子中，摘要輸出是 '>摘要（svmfit）呼叫： SVM（式=默認〜，數據= lc_train，內核=「線性」，成本= 10 ，標度= FALSE）參數： SVM-類型：C-分類 SVM-內核：線性成本：10 伽瑪：0.5 支持向量的數量：192 （92 100）數類別：2 級別： -1 1'。總結輸出是否意味着有192個支持向量，其中一個類有92個，另一個有100個？爲什麼預測同一個'lc_train'預測全部在一個類中？ – Vincent

據我所知，支持向量定義了一個餘量。這並不意味着它們是相應類的元素。在這裏他們定義了一個邊界，將數據集中的所有元素分配給一個類。 'plot（svmfit，lc_train）'可以幫助你看到結果。順便說一下，如此大量的支持向量是一個不好的跡象（http://stackoverflow.com/a/9485280/4973476）。 –

SVM圖書館e1071（預測功能總是返回一個類）中的R

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