Hyperplanes can be imagined as decision boundaries that can be used to classify the data points from a dataset, which follows p - 1. Data points that are on either side can be categorized to their respective class. In addition, hyperplanes can be one or two dimensional, depending on how many input features are used. When there are two features, the hyperplane is a line (2 - 1 where p = 2) and when there are 3 then it is a flat two-dimensional plane (3 - 1 where p = 3). If there are more than 3 features (p > 3), it is very hard to conceptually visualize a hyperplane but p - 1 dimensions still apply.