ValentinMargraf/ActiveLearningPipelines
Specificy, execute and monitor performances of active learning pipelines.
This tool helps machine learning engineers and researchers to set up, run, and evaluate active learning pipelines for tabular classification tasks. You input your desired learning algorithm and query strategy, and it outputs performance metrics and ensures reproducible evaluation of different active learning approaches. It's designed for those comparing and benchmarking active learning methods.
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Use this if you need to systematically benchmark different active learning strategies and learning algorithms on a variety of tabular datasets, ensuring fair and reproducible comparisons.
Not ideal if you are looking for a simple, out-of-the-box solution to apply active learning without needing to compare or evaluate multiple pipeline configurations.
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Language
Python
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Last pushed
Oct 01, 2024
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