Project for the class "Techniques of Artificial Intelligence" undertaken as part of a Master of Science in Geography at KU Leuven/Vrije Universiteit Brussel. Implements a decision tree classifier, as well as a bagged classifier. By changing the import to "DecisionTree_RF" a random forest classification can also be completed. Based on [Jason Brownlee's guide on how to build a decision tree from scratch in Python] (https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/), with additional extensions aiming to incorporate ensemble methods and utilizing an altered approach to tree construction.
"Samuel_Oswald_Report_TechAI" describes in detail the approach undertaken to construct this classifier.
Use "Run.py" to run the model upon download of the repository. Edit this file to specify test and training set name (see data folder for included datasets), and if using the APEX dataset uncomment lower lines of code (will additionally require matplotlib and spectral libraries).