Pruning Hoeffding Trees by the Importance of Features

This repository contains the official code for our workshop paper. It contains Pruning Hoeffding Trees by the Importance of Features (ProeFI), a novel approach that, in a transparent manner, prunes HT to mitigate unbounded growth and enhance adaptability to evolving data. ProeFI employs incremental permutation feature importance and a self-adaptive threshold to dynamically refine its pruning process in response to drifting data distributions. The implementation is based on river and ixai. ProeFI is implemented as a Hoeffding tree classifier and should support all river functionalities.

Workshop paper: "Transparent and Adaptive Pruning of Hoeffding Trees" Published at the 2nd TempXAI Workshop on Explainable AI in Time Series and Data Streams, ECML-PKDD 2025

Installation

ProeFI requires Python 3.8 or above. Installation of the requirements can be done via pip:

pip install -r requirements.txt 

Example Code

The following code shows one way to train & evaluate ProeFI on Agrawal with an abrupt concept drift.

>>> from river import metrics

>>> from tree import ProeFI
>>> from data.datasets.experiment_datasets import DriftingAgrawal

>>> dataset = DriftingAgrawal(width=50)
>>> model = ProeFI()
>>> metric = metrics.ROCAUC()

>>> for x, y in dataset.take(10000):
>>>   y_pred = model.predict_proba_one(x)
>>>   metric.update(y, y_pred)
>>>   model.learn_one(x, y)

Reproducibility

Reproduce paper results with:

bash ./paper_results.sh 

Citation

If you use ProeFI in your research, please cite:

@InProceedings{10.1007/978-3-032-19105-2_30,
  author="Händler, Kevin and Köbschall, Kirsten and Cerrato, Mattia and Kramer, Stefan",
  editor="Koprinska, Irena and Mendes-Moreira, João and Branco, Paula",
  title="Transparent and Adaptive Pruning of Hoeffding Trees",
  booktitle="Machine Learning and Principles and Practice of Knowledge Discovery in Databases",
  year="2026",
  publisher="Springer Nature Switzerland",
  address="Cham",
  pages="428--441",
  doi="10.1007/978-3-032-19105-2_30",
  isbn="978-3-032-19105-2"
}