LANDMarkClassifier 2.1.1

LANDMark

Implementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.

Install

From PyPI:

pip install LANDMarkClassifier

From source:

git clone https://github.com/jrudar/LANDMark.git
cd LANDMark
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .

Interface

An overview of the API can be found here.

Usage and Examples

Examples of how to use LANDMark can be found here.

Contributing

To contribute to the development of LANDMark please read our contributing guide

Projects Using LANDMark

Rudar J, Kruczkiewicz P, Vernygora O, Golding GB, Hajibabaei M, Lung O. Sequence signatures 
within the genome of SARS-CoV-2 can be used to predict host source. Microbiol Spectr. 
2024 Apr 2;12(4):e0358423. doi: 10.1128/spectrum.03584-23. Epub 2024 Mar 4. PMID: 38436242.

Rudar J, Golding GB, Kremer SC, Hajibabaei M. Decision Tree Ensembles Utilizing Multivariate 
Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon 
Sequencing Data. Microbiol Spectr. 2023 Mar 6;11(2):e0206522. doi: 10.1128/spectrum.02065-22. 
Epub ahead of print. PMID: 36877086; PMCID: PMC10100742.

Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble 
approach to the supervised selection of biomarkers in high-throughput sequencing data. 
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z

References

Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble 
approach to the supervised selection of biomarkers in high-throughput sequencing data. 
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: 
Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30. 

Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle. 
IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500–8. 

Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.