[PRE REVIEW]: ControlBurn: Explainable Feature Selection with Sparse Forests in Python

[editorialbot]

Submitting author: !--author-handle-->@brianliu12437<!--end-author-handle-- (Brian Liu) Repository: https://github.com/udellgroup/controlburn Branch with paper.md (empty if default branch): JOSS_submission Version: v0.1.3 Editor: Pending Reviewers: Pending Managing EiC: Arfon Smith

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.88  T=0.06 s (306.0 files/s, 132067.4 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                           4            667            719           2443
Jupyter Notebook                 7              0           1547           1246
HTML                             1             78              4            966
TeX                              1             33              0            271
Markdown                         4             69              0            129
YAML                             1              1              4             18
TOML                             1              0              0              6
-------------------------------------------------------------------------------
SUM:                            19            848           2274           5079
-------------------------------------------------------------------------------

gitinspector failed to run statistical information for the repository

[editorialbot]

Wordcount for paper.md is 1262

[editorialbot]

Failed to discover a valid open source license

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.5281/zenodo.6053272 is OK

MISSING DOIs

- 10.1007/978-3-030-56485-8_3 may be a valid DOI for title: Random forests
- 10.1080/00401706.1995.10484371 may be a valid DOI for title: Better subset regression using the nonnegative garrote
- 10.18637/jss.v033.i01 may be a valid DOI for title: Regularization paths for generalized linear models via coordinate descent
- 10.1007/bf00058655 may be a valid DOI for title: Bagging predictors
- 10.1016/0304-3835(94)90099-x may be a valid DOI for title: Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates
- 10.1249/00005768-198504000-00037 may be a valid DOI for title: Generalized body composition prediction equation for men using simple measurement techniques
- 10.1214/15-aos1388 may be a valid DOI for title: Best subset selection via a modern optimization lens
- 10.1214/20-sts807 may be a valid DOI for title: Discussion of “best subset, forward stepwise or lasso? analysis and recommendations based on extensive comparisons”
- 10.1016/s0377-2217(01)00264-8 may be a valid DOI for title: A data-driven software tool for enabling cooperative information sharing among police departments
- 10.1145/2339530.2339556 may be a valid DOI for title: Intelligible models for classification and regression
- 10.1016/j.csda.2006.12.019 may be a valid DOI for title: Relaxed lasso
- 10.1186/s12863-018-0633-8 may be a valid DOI for title: Using recursive feature elimination in random forest to account for correlated variables in high dimensional data

INVALID DOIs

- None

[editorialbot]

:point_right::page_facing_up: Download article proof :page_facing_up: View article proof on GitHub :page_facing_up: :point_left:

[editorialbot]

Five most similar historical JOSS papers:

RENT: A Python Package for Repeated Elastic Net Feature Selection Submitting author: @annajenul Handling editor: @mikldk (Retired) Reviewers: @maximtrp, @arunmano121 Similarity score: 0.8017

Groupyr: Sparse Group Lasso in Python Submitting author: @richford Handling editor: @galessiorob (Active) Reviewers: @janfreyberg, @JonathanReardon, @rougier Similarity score: 0.8014

LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility Submitting author: @kiretd Handling editor: @dfm (Active) Reviewers: @effigies, @sauln Similarity score: 0.7897

Yellowbrick: Visualizing the Scikit-Learn Model Selection Process Submitting author: @bbengfort Handling editor: @arfon (Active) Reviewers: @mnarayan Similarity score: 0.7864

UBayFS: An R Package for User Guided Feature Selection Submitting author: @annajenul Handling editor: @osorensen (Active) Reviewers: @dhvalden, @aaronpeikert, @EugeneHao Similarity score: 0.7812

⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before before considering asking the reviewers of these papers to review again for JOSS.

[arfon]

@editorialbot query scope

@brianliu12437 – this submission looks a but unusually packaged and may require quite a lot of work to get into shape to be ready for a JOSS review. As such, I'm going to ask the JOSS editorial team to check the scope of this submission. This may take a couple of weeks to complete.

[editorialbot]

Submission flagged for editorial review.

[arfon]

@editorialbot reject

@brianliu12437 – this submission is considered out of scope at this point as it's not ready to be reviewed by JOSS. If you wish to resubmit to JOSS, please familiarize yourself with the Python packaging guidelines and update your software accordingly (and then resubmit).

[editorialbot]

Paper rejected.