A command-line binary machine learning classifier built from scratch in C++. Users can load in a labelled training dataset, which the program uses to grow a binary classification tree by recursively finding optimal data splits based on entropy impurity. Once trained, the classifier can predict the category of new, unseen observations.
Designed with a focus on OOP principles, recursion, and data structures.