support vector machine train caret error kernlab class probability calculations failed; returning NAs

r, r-caret, testing

Solution

In the train control statement, you have to specify if you want the class probabilities `classProbs = TRUE` returned.

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")

giving the probabilities of being in the Bad or Good class in the test dataset as:

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

EDIT

To answer your new question, you can access the position of the support vectors in your original data set with `alphaindex(svmFit$finalModel)` with coefficients `coef(svmFit$finalModel)`.

Problem

i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the probability of getting 'good' ...generally in case of support vector machine, model will calculate probability of prediction..if Y variable has 2 outcomes then model will predict probability of each outcome. The outcome which has the maximum probability is considered as the final solution) ``` **Warning message: In probFunction(method, modelFit, ppUnk) : kernlab class probability calculations failed; returning NAs** ``` sample code as below ``` 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) ``` new question below this line: as per below output there are 9 support vectors. how to recognize out of 12 training data points which are those 9? ``` svmFit$finalModel ``` Support Vector Machine object of class "ksvm" SV type: C-svc (classification) parameter : cost C = 1 Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.72640759446315 Number of Support Vectors : 9 Objective Function Value : -5.6994 Training error : 0.083333

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