# YAML 1.2
---
# Metadata for citation of this software according to the CFF format (https://citation-file-format.github.io/)
cff-version: 1.2.0
title: "treeple: Modern decision-trees compatible with scikit-learn in Python."
abstract: "treeple is a scikit-learn compatible API for building state-of-the-art decision trees. These include unsupervised trees, oblique trees, uncertainty trees, quantile trees and causal trees."
authors:
  - given-names: Adam
    family-names: Li
    affiliation: "Department of Computer Science, Columbia University, New York, NY, USA"
    orcid: "https://orcid.org/0000-0001-8421-365X"
  - given-names: Sambit
    family-names: Panda
    affiliation: "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"
    orcid: "https://orcid.org/0000-0001-8455-4243"
  - given-names: Haoyin
    family-names: Xu
    affiliation: "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"
    orcid: "https://orcid.org/0000-0001-8235-4950"
  - given-names: Itsuki
    family-names: Ogihara
    affiliation: "Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA"
type: software
repository-code: "https://github.com/neurodata/treeple"
license: 'PolyForm-Noncommercial-1.0.0'
keywords:
  - random forest
  - oblique trees
  - honest forests
  - statisical learning
  - machine learning
message: >-
  Please cite this software using the metadata from
  'preferred-citation' in the CITATION.cff file.
