TinyDecisionTreeClassifier is a simple but elegant standalone library for training decision trees directly on the edge. Decision trees were chosen since they are very fast and easily interpretable.

Main features are:

• Based around classic C4.5 decision tree algorithm for continuous variables.
• This is a standalone library. Since the library depends only on <stdint.h>, <stddef.h>,<stdlib.h>, “math.h” and “float.h” it can easily be ported to other frameworks like Mbed/ESP-IDF.
• Fast and small. Checkout the benchmarking examples for Arduino Uno, Esp32 and NRF52840 provided in examples folder and the benchmarking graph.
• Simple to use. I tried to make the methods similar to the DecisionTreeClassifier from scikit-learn. If you ever used it, you will quickly recognize the familiar names like fit(), predict() and score().
• The average complexity is O(Nlog(N)) (because of the quicksort).
• Template usage allows to train 8-bit models, which boosts performance on 8-bit MCUs and significantly reduces RAM requirements.
• Trained tree visualisation is supported via plot() method.

See more in GitHub.