While the term "Pokémon trainer" typically refers to someone training their Pokémon, the trainer themself also goes through a dynamic learning process. People gain expertise through a cycle of watching and practicing. They internalize data, attempt to replicate it, then repeat the process to understand the implicit rules of success. For many years, I have played the online two-player game "Pokémon Showdown". To master the battlefield like great competitive players, I decided to create a model to break down successful patterns by offloading the "watching" part of learning to a custom-built machine learning algorithm. Called a "classifying function", this algorithm is trained on the basic building blocks of a Pokémon battle, like names and abilities, taken from top-ranked players. Encoded data is transformed by a chain of "layers", then decoded to give a result. For this specific classifier, the input parameters are the battlefield state, and the output is a list of actions to be taken by each player (with assumed optimality). The function extends and generalizes the concepts at play in oversaturated usage out to all potential Pokémon. As an on-site placement, I worked virtually with a data analyst who created a website for analyzing trends in Pokémon usage, where I helped design the graphical user interface. Under the classifier's guidance, aspiring trainers will more easily understand how to win in any circumstance. If "truly skilled trainers should try to win with their favorites" (Pokémon Crystal), I want to provide a resource to help people achieve that.