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Visualizing decision & margin bounds using `plot()`

In this exercise, you will rebuild the SVM model (as a refresher) and use the built in SVM plot() function to visualize the decision regions and support vectors. The training data is available in the dataframe trainset.

Cet exercice fait partie du cours

Support Vector Machines in R

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Instructions

  • Load the library needed to build an SVM model.
  • Build a linear SVM model using the training data.
  • Plot the decision regions and support vectors.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

#load required library
library(___)

#build svm model
svm_model<- 
    svm(y ~ ., data = ___, type = "C-classification", 
        kernel = "___", scale = FALSE)

#plot decision boundaries and support vectors for the training data
plot(x = svm_model, data = ___)
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