A standalone library for training decision trees on microcontrollers
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.
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