data-centric-ai/dcbench
A benchmark of data-centric tasks from across the machine learning lifecycle.
This project helps machine learning researchers and practitioners evaluate different methods for improving machine learning models by focusing on the data itself, rather than just the model. It takes in datasets and various data manipulation techniques (like cleaning or subsetting) and outputs performance metrics, helping you compare which data-centric approaches work best for your specific problem. It's for anyone building or deploying ML systems who wants to improve their model's reliability and performance by refining the input data.
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Use this if you need a standardized way to compare different data preparation, feature engineering, or data selection strategies for your machine learning projects.
Not ideal if you are looking for a tool to train or fine-tune machine learning models themselves, as its focus is exclusively on evaluating data-centric tasks.
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71
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Language
Jupyter Notebook
License
Apache-2.0
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Last pushed
Jun 08, 2022
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