Overview

Streaming Decision Trees & Forests: exploring streaming options for decision trees and random forests.

The package includes two ensemble implementations (Stream Decision Forest and Cascade Stream Forest).

Based on scikit-learn fork.

Install

You can manually download the latest version of SDTF by cloning the repository:

git clone https://github.com/neurodata/SDTF
cd SDTF
python setup.py install

Or install the stable version through pip:

pip install sdtf

Package Requirements

The SDTF package requires a scikit-learn fork for the partial_fit functionality, which you can install manually:

git clone https://github.com/PSSF23/scikit-learn-stream
cd scikit-learn-stream
python setup.py install

The above local setup requires the following packages:

• cython
• numpy
• scipy

Relevant Repos

• online-ml/river
• scikit-garden/scikit-garden
• huawei-noah/streamDM
• soundcloud/spdt

Relevant Papers

• Very Fast Decision Tree
• Mondrian Forests
• Online Bagging and Boosting
• Leveraging Bagging for Evolving Data Streams
• Ensemble Learning for Data Stream Classification
• Adaptive Random Forests
• Streaming Random Forests
• Streaming Parallel Decision Tree

Benchmarks

.. toctree::
  :maxdepth: 1

  iris
  select
  cc18
  xor_experiments

API

.. toctree::
  :maxdepth: 1

  api