论文标题
Deepnecks：用于测试和验证机器学习模型和数据的库
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
论文作者
论文摘要
本文介绍了Deepchecks，这是一个用于全面验证机器学习模型和数据的Python库。我们的目标是提供一个易于使用的库，其中包括许多与各种类型问题有关的检查，例如模型预测性能，数据完整性，数据分发不匹配等等。该软件包分布在GNU Affero通用公共许可证（AGPL）下，依赖于科学Python生态系统的核心库：Scikit-Learn，Pytorch，Numpy，Pandas和Scipy。源代码，文档，示例和广泛的用户指南可以在\ url {https://github.com/deepchecks/deepchecks}和\ url {https://docs.deeps.deepchecks.com/}中找到。
This paper presents Deepchecks, a Python library for comprehensively validating machine learning models and data. Our goal is to provide an easy-to-use library comprising of many checks related to various types of issues, such as model predictive performance, data integrity, data distribution mismatches, and more. The package is distributed under the GNU Affero General Public License (AGPL) and relies on core libraries from the scientific Python ecosystem: scikit-learn, PyTorch, NumPy, pandas, and SciPy. Source code, documentation, examples, and an extensive user guide can be found at \url{https://github.com/deepchecks/deepchecks} and \url{https://docs.deepchecks.com/}.
