BaşlayınÜcretsiz Başlayın

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.

Bu egzersiz

Support Vector Machines in R

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • 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.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

#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 = ___)
Kodu Düzenle ve Çalıştır