Aminsn/sert
sert: deep learning on sets
This tool helps data analysts and machine learning practitioners build predictive models from complex, irregular datasets. It takes 'long-format' data, like log entries or non-aligned time series, and outputs classifications or predictions. It's especially useful for those working with data that has many missing values or is difficult to structure into traditional tables.
No commits in the last 6 months.
Use this if you need to make predictions or classify data from datasets that are best represented as a collection of individual observations, such as log data, sparse tabular data, or multivariate time series where measurements don't perfectly align.
Not ideal if your data is already in a clean, complete, wide-format table and you prefer traditional machine learning methods that require full rows of observations.
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9
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Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Sep 09, 2023
Commits (30d)
0
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