How to find out why svm considers a particular observation a support vector?












1















Following an example in An Introduction to Statistical Learning (James et al. 2015) I trained a Support Vector Machine on some dummy data:



set.seed(1)

x <- matrix(rnorm(40), ncol = 2)

y <- c(rep(-1, 10), rep(1, 10))

data <- data.frame(x = x, y = as.factor(y))

svmfit <- svm(y ~ ., data = data, kernel = "linear", cost = 10, scale = F)

plot(svmfit, data, xlim = c(-3, 3), ylim = c(-3, 3))


This gives:



enter image description here



In this plot, according to ?plot.svm support vectors are identified by an 'x'. My question is: why is the yellow circled observation a support vector whilst other observations clearly closer to the decision boundary (e. g. the blue circled one) are not?






r svm







share|improve this question



























    1















    Following an example in An Introduction to Statistical Learning (James et al. 2015) I trained a Support Vector Machine on some dummy data:



    set.seed(1)
    
x <- matrix(rnorm(40), ncol = 2)
    
y <- c(rep(-1, 10), rep(1, 10))
    
data <- data.frame(x = x, y = as.factor(y))
    
svmfit <- svm(y ~ ., data = data, kernel = "linear", cost = 10, scale = F)
    
plot(svmfit, data, xlim = c(-3, 3), ylim = c(-3, 3))


    This gives:



    enter image description here



    In this plot, according to ?plot.svm support vectors are identified by an 'x'. My question is: why is the yellow circled observation a support vector whilst other observations clearly closer to the decision boundary (e. g. the blue circled one) are not?






    r svm







    share|improve this question

























      1












      1








      1








      Following an example in An Introduction to Statistical Learning (James et al. 2015) I trained a Support Vector Machine on some dummy data:



      set.seed(1)
      
x <- matrix(rnorm(40), ncol = 2)
      
y <- c(rep(-1, 10), rep(1, 10))
      
data <- data.frame(x = x, y = as.factor(y))
      
svmfit <- svm(y ~ ., data = data, kernel = "linear", cost = 10, scale = F)
      
plot(svmfit, data, xlim = c(-3, 3), ylim = c(-3, 3))


      This gives:



      enter image description here



      In this plot, according to ?plot.svm support vectors are identified by an 'x'. My question is: why is the yellow circled observation a support vector whilst other observations clearly closer to the decision boundary (e. g. the blue circled one) are not?






      r svm







      share|improve this question














      Following an example in An Introduction to Statistical Learning (James et al. 2015) I trained a Support Vector Machine on some dummy data:



      set.seed(1)
      
x <- matrix(rnorm(40), ncol = 2)
      
y <- c(rep(-1, 10), rep(1, 10))
      
data <- data.frame(x = x, y = as.factor(y))
      
svmfit <- svm(y ~ ., data = data, kernel = "linear", cost = 10, scale = F)
      
plot(svmfit, data, xlim = c(-3, 3), ylim = c(-3, 3))


      This gives:



      enter image description here



      In this plot, according to ?plot.svm support vectors are identified by an 'x'. My question is: why is the yellow circled observation a support vector whilst other observations clearly closer to the decision boundary (e. g. the blue circled one) are not?






      r svm




      r svm






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 22 '18 at 17:15









      JoeJoe

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