We investigate three different ways of solving classification problems: logistic regression, K-nearest neighbors (KNN), and decision trees. Using a simple ER readmissions example, we visualize the decision boundaries produced by each of these algorithms and discuss their advantages and disadvantages. Key terms: training and test data, feature, feature space, extrapolation, decision boundary, hyperparameter.