sdtf 0.0.7

Exploring streaming options for decision trees and random forests. Based on scikit-learn fork.

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/neurodata/scikit-learn -b stream --single-branch
cd scikit-learn
python setup.py install

The above local setup requires the following packages:

• cython
• numpy
• scipy

Relevant Repos

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

Relevant Papers

• Very Fast Decision Tree
• 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
• Mondrian Forests