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
andcost = 100
classifiers insvm_model_1
andsvm_model_100
, respectively. - The slope and intercept for the
cost = 1
classifier is stored inslope_1
andintercept_1
. - The slope and intercept for the
cost = 100
classifier is stored inslope_100
andintercept_100
. - Weight vectors for the two costs are stored in
w_1
andw_100
, respectively - A basic scatter plot of the training data is stored in
train_plot
The ggplot2
library has been preloaded.
This exercise is part of the course
Support Vector Machines in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
#add decision boundary and margins for cost = 1 to training data scatter plot
train_plot_with_margins <- train_plot +
geom_abline(slope = ___, intercept = ___) +
geom_abline(slope = ___, intercept = ___-1/w_1[2], linetype = "dashed")+
geom_abline(slope = ___, intercept = ___+1/w_1[2], linetype = "dashed")
#display plot
train_plot_with_margins