The Neural Decision Tree (NDT) is a hybrid supervised machine-learning algorithm that combines the self-limiting property of a decision tree (CART) algorithm with the artificial neural network (ANN). We demonstrate the use of NDT for a regression problem of building a prediction model for the plasmasphere electron density with solar and geomagnetic measurements as inputs. Our work replicates the work by Zhelavskaya et al. reported in their 2017 article to show that NDT makes available sophisticated network layout for building a predictive model, thus taking advantage of the deep-learning potential of the neural network. We also demonstrate that with the ability to automatically select an appropriate network layout, as well as, effective initialization, the NDT algorithm allows research scientists in space weather to focus more of their attention on physically and statistically relevant aspects of using machine-learning techniques. In fact, our example highlights the fact that the basic assumptions of standard supervise machine-learning problems are often unsatisfied in real-world space weather applications. Greater attention to these fundamental issues may create significantly different solutions to space weather forecast problems.