R Tutorial: Generating a linearly separable dataset

Want to learn more? Take the full course at https://learn.datacamp.com/courses/support-vector-machines-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the previous lesson, we used a simple one dimensional example to illustrate the notion of an optimal decision boundary, that is, one that maximizes the margin. In this lesson we'll create a two-predictor dataset that we will subsequently use to illustrate some of the key principles of support vector machines, including margin maximization. The dataset we will generate is essentially a generalization of the previous example in that it has two variables instead of one and the decision boundary is a line rather than a point. We generate a dataset with 200 points consisting of two predictor variables, x1 and x2, that are uniformly distributed between 0 and 1. To do this we first set the number of data points n to 200 and the seed integer for random number generation. We then create two sets of random numbers lying between 0 and 1 using the runif() function, which generates uniform random numbers. The resulting values for x1 and x2 are stored in the dataframe df. Next we create two classes separated by a straight line x1=x2. This line passes through the origin and makes an angle of 45 degrees with the horizontal axis. We label points below the line as having class equals -1 and those above as having class equals +1. Here's the code. Now let's see what our two class dataset looks like. Let's visualize the dataset and the decision boundary using ggplot(). We'll create a two dimensional scatter plot with x1 on the x-axis and x2 on the y-axis, distinguishing the two classes by color. Points below the decision boundary will be colored red and those above blue. The decision boundary itself is a straight line x1 equals x2, which passes through the origin and has a slope of 1, that is, it makes an angle 45 degrees with the x1 axis. Here's the code. An