The iris data set gives the measurements in centimeters of the variables sepal length and width, and petal length and width, respectively, for 500 flowers from each of 3 species of iris. The species are Iris setosa, versicolor and virginica.

The dataframe consists of 150 cases (rows) and 5 variables (columns) named Sepal.Length, Sepal.Width, Petal.Length, Petal.Width and Species.

dim(iris)
[1] 150   5

names(iris)
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"     

head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Questions

• Create a 70-30 train-test split with of the iris data set (for this you can use the sample() function). Store the training data in train and test data in test.
• On the training data, perform 10-fold cross-validation to determine the best value of the cost parameter with a range of (0.001, 0.01, 0.1, 1, 10, 100).
  • Use Species as the dependent variable and the other variables as the independent variables.
  • Scale the independent variables and use a linear kernel.
  • Store the outcome of the cross-validation in tune.out.
  • Store the best performing model in bestmod.
• Calculate the accuracy on the test data of the best model. Store the accuracy in score.

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Assume that:

• The e1071 library has been loaded
• The iris data set has been loaded and attached