LeakageDetector: An Open Source Data Leakage Analysis Tool in Machine Learning Pipelines
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909542269321216 |
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| author | AlOmar, Eman Abdullah DeMario, Catherine Shagawat, Roger Kreiser, Brandon |
| author_facet | AlOmar, Eman Abdullah DeMario, Catherine Shagawat, Roger Kreiser, Brandon |
| contents | Code quality is of paramount importance in all types of software development settings. Our work seeks to enable Machine Learning (ML) engineers to write better code by helping them find and fix instances of Data Leakage in their models. Data Leakage often results from bad practices in writing ML code. As a result, the model effectively ''memorizes'' the data on which it trains, leading to an overly optimistic estimate of the model performance and an inability to make generalized predictions. ML developers must carefully separate their data into training, evaluation, and test sets to avoid introducing Data Leakage into their code. Training data should be used to train the model, evaluation data should be used to repeatedly confirm a model's accuracy, and test data should be used only once to determine the accuracy of a production-ready model. In this paper, we develop LEAKAGEDETECTOR, a Python plugin for the PyCharm IDE that identifies instances of Data Leakage in ML code and provides suggestions on how to remove the leakage. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LeakageDetector: An Open Source Data Leakage Analysis Tool in Machine Learning Pipelines AlOmar, Eman Abdullah DeMario, Catherine Shagawat, Roger Kreiser, Brandon Software Engineering Code quality is of paramount importance in all types of software development settings. Our work seeks to enable Machine Learning (ML) engineers to write better code by helping them find and fix instances of Data Leakage in their models. Data Leakage often results from bad practices in writing ML code. As a result, the model effectively ''memorizes'' the data on which it trains, leading to an overly optimistic estimate of the model performance and an inability to make generalized predictions. ML developers must carefully separate their data into training, evaluation, and test sets to avoid introducing Data Leakage into their code. Training data should be used to train the model, evaluation data should be used to repeatedly confirm a model's accuracy, and test data should be used only once to determine the accuracy of a production-ready model. In this paper, we develop LEAKAGEDETECTOR, a Python plugin for the PyCharm IDE that identifies instances of Data Leakage in ML code and provides suggestions on how to remove the leakage. |
| title | LeakageDetector: An Open Source Data Leakage Analysis Tool in Machine Learning Pipelines |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.14723 |