Exercise

# Visualizing decision boundaries and margins

In the previous exercise you built two linear classifiers for a linearly separable dataset, one with `cost = 1`

and the other `cost = 100`

. In this exercise you will visualize the margins for the two classifiers on a single plot. The following objects are available for use:

- The training dataset:
`trainset`

. - The
`cost = 1`

and`cost = 100`

classifiers in`svm_model_1`

and`svm_model_100`

, respectively. - The slope and intercept for the
`cost = 1`

classifier is stored in`slope_1`

and`intercept_1`

. - The slope and intercept for the
`cost = 100`

classifier is stored in`slope_100`

and`intercept_100`

. - Weight vectors for the two costs are stored in
`w_1`

and`w_100`

, respectively - A basic scatter plot of the training data is stored in
`train_plot`

The `ggplot2`

library has been preloaded.

Instructions 1/2

**undefined XP**

- Add the decision boundary and margins for the cost = 1 classifier to the training data plot.
- Display the resulting plot.