seedatnabeel/Data-IQ
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data (NeurIPS 2022)
This tool helps data scientists and machine learning engineers understand the reliability of their tabular datasets. By analyzing how your machine learning model learns, it classifies individual data points into 'easy,' 'ambiguous,' or 'hard' subgroups. This provides insights into which parts of your data your model struggles with or is uncertain about, helping you refine your dataset and improve model performance.
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Use this if you are building or training machine learning models on tabular data and need to understand why your model performs well on some data points and poorly on others.
Not ideal if you are working with non-tabular data types like images, text, or audio, or if you don't use iterative machine learning models.
Stars
18
Forks
6
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 20, 2023
Commits (30d)
0
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