Reviewer name and names of any other individual's who aided in reviewer	Shuangsang Fang
Do you understand and agree to our policy of having open and named reviews, and having your review included with the published manuscript. (If no, please inform the editor that you cannot review this manuscript.)	Yes
Is the language of sufficient quality?	Yes
Please add additional comments on language quality to clarify if needed
Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?	Yes
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Is the source code available, and has an appropriate Open Source Initiative license <a href="https://opensource.org/licenses" target="_blank">(https://opensource.org/licenses)</a> been assigned to the code?	Yes
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As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code?	Yes
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Is the code executable?	Unable to test
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Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?	Unable to test
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Is the documentation provided clear and user friendly?	Yes
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Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level?	Yes
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Have any claims of performance been sufficiently tested and compared to other commonly-used packages?	Yes
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Is test data available, either included with the submission or openly available via cited third party sources (e.g. accession numbers, data DOIs)?	Yes
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Are there (ideally real world) examples demonstrating use of the software?	Yes
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Additional Comments
Any Additional Overall Comments to the Author	The paper titled "Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models" by Sami Hamdan et al. introduces a library that empowers researchers to design and evaluate complex ML pipelines. This library provides users with a user-friendly environment that incorporates safeguards against common ML pitfalls. Consequently, it offers convenience and usefulness to its users. Nonetheless, there are a few concerns that need to be addressed: 1.The authors should clearly articulate the relationship between Julearn and Scikit-learn (sklearn) and perform a comparative analysis of their shared and distinctive features. 2.It would be beneficial to include a table or figure that provides a comprehensive list of functions or ML models available in Julearn, enabling users to quickly familiarize themselves with the library's capabilities. 3.While the Visualization component of Julearn currently only offers the "plot_scores" function, the inclusion of additional plotting functions would be advantageous in providing users with a more comprehensive visualization toolkit. By addressing these concerns, the usability and effectiveness of Julearn can be further enhanced, ensuring a more robust and user-friendly experience for researchers utilizing the library.
Recommendation	Minor Revisions