dholzmueller/bmdal_reg
Deep Batch Active Learning for Regression
This project helps machine learning practitioners efficiently train regression models when labeled data is scarce. It takes in large tabular datasets with limited labeled examples and uses advanced 'active learning' strategies to intelligently select which additional examples should be labeled next. The output is a more accurate regression model, built with fewer costly labeled examples, making model development faster and more cost-effective for end-users like data scientists or researchers.
No commits in the last 6 months. Available on PyPI.
Use this if you are building regression models but face high costs or significant effort in acquiring enough labeled data for training.
Not ideal if you already have abundant labeled data for your regression task or if your problem involves classification rather than predicting continuous values.
Stars
73
Forks
6
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 03, 2024
Commits (30d)
0
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