An Interactive Web Application for Decision Tree Learning
Decision tree learning offers an intuitive and straightforward introduction to machine learning techniques, especially when students are used to program imperative code. Most commonly, trees are trained using a greedy algorithm based on information-theoretic criteria. While there are many static resources such as slides or animations out there, interactive visualizations tend to be based on somewhat outdated UI technology and dense in information. We propose a clean and simple web application for decision tree learning that is extensible and open source.
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