Binary classification with linear classifiers is a fundamental problem in machine learning with broad applications. Traditionally, this problem focuses on using labeled data to learn a linear classifier, but data is often expensive to label or difficult to acquire. Instead of labeling all data points in a data set, we consider labeling a specially chosen subset of points and ask how well we can accomplish our learning task. To answer this question, we will reformulate this problem into the context of Gaussian spherical tessellations and study geometric properties of such tessellations. This work is joint with Rayan Saab and Eric Lybrand.