支持向量机火车插入符错误kernlab类概率计算失败; 返回NAs

Question

我有一些数据,Y变量是一个因素 - 好或坏.我正在使用'caret'包中的'train'方法构建一个支持向量机.使用'train'功能,我能够最终确定各种调整参数的值,并获得最终的支持向量机.对于测试数据,我可以预测"类".但是当我试图预测测试数据的概率时,我得到的误差低于(例如我的模型告诉我测试数据中的第一个数据点y ='good',但我想知道获得'好'的概率是多少...通常在支持向量机的情况下,模型将计算预测的概率.如果Y变量有2个结果,则模型将预测每个结果的概率.具有最大概率的结果被认为是最终解决方案)

**Warning message:  
In probFunction(method, modelFit, ppUnk) :  
  kernlab class probability calculations failed; returning NAs**

示例代码如下

library(caret)
trainset <- data.frame( 
     class=factor(c("Good",    "Bad",   "Good", "Good", "Bad",  "Good", "Good", "Good", "Good", "Bad",  "Bad",  "Bad")),
     age=c(67,  22, 49, 45, 53, 35, 53, 35, 61, 28, 25, 24))

testset <- data.frame( 
     class=factor(c("Good",    "Bad",   "Good"  )),
    age=c(64,   23, 50))



library(kernlab)
set.seed(231)

### finding optimal value of a tuning parameter
sigDist <- sigest(class ~ ., data = trainset, frac = 1)
### creating a grid of two tuning parameters, .sigma comes from the earlier line. we are trying to find best value of .C
svmTuneGrid <- data.frame(.sigma = sigDist[1], .C = 2^(-2:7))

set.seed(1056)
svmFit <- train(class ~ .,
                data = trainset,
                method = "svmRadial",
                preProc = c("center", "scale"),
                tuneGrid = svmTuneGrid,
                trControl = trainControl(method = "repeatedcv", repeats = 5))

### svmFit finds the optimal values of tuning parameters and builds the model using the best parameters

### to predict class of test data
predictedClasses <- predict(svmFit, testset )
str(predictedClasses)


### predict probablities but i get an error
predictedProbs <- predict(svmFit, newdata = testset , type = "prob")
head(predictedProbs)

这一行下面的新问题:按照以下输出,有9个支持向量.如何识别12个训练数据点是哪9个？

svmFit$finalModel

支持类"ksvm"的向量机对象

SV类型:C-svc(分类)参数:成本C = 1

高斯径向基核函数.超参数:sigma = 0.72640759446315

支持向量数量:9

目标函数值:-5.6994训练错误:0.083333

Answer 1

在列车控制语句中,您必须指定是否要classProbs = TRUE返回类概率.

svmFit <- train(class ~ .,
    data = trainset,
    method = "svmRadial",
    preProc = c("center", "scale"),
    tuneGrid = svmTuneGrid,
    trControl = trainControl(method = "repeatedcv", repeats = 5, 
classProbs =  TRUE))

predictedClasses <- predict(svmFit, testset )
predictedProbs <- predict(svmFit, newdata = testset , type = "prob")

给出测试数据集中Bad或Good类的概率:

print(predictedProbs)
    Bad      Good
1 0.2302979 0.7697021
2 0.7135050 0.2864950
3 0.2230889 0.7769111

编辑

要回答新问题,可以alphaindex(svmFit$finalModel)使用系数访问原始数据集中支持向量的位置coef(svmFit$finalModel).