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A doctor who needs to choose which test to perform on their patient and an agent considering whether a certain behavior on a network is caused by a hacker are examples of individuals confronted with such situations. However, taking a decision too hastily may lead to more mistakes, resulting in additional costs that could have been avoided.  In these situations, where there is a trade-off between the *earliness* of the decision and the *accuracy* of the prediction, the **Machine Learning for Early Decision Making** (ML-EDM) framework offers ML solutions not only to make a prediction but also to decide when to trigger its associated decision timely.\r\n\r\nThe ``ml_edm`` package provides tools to facilitate dealing with the **Early Classification of Time Series** (ECTS) problem, whose goal is to determine the class associated with a time series before all measurements are available.\r\n\r\n## Install \r\n\r\n```console\r\n# Original library:\r\npip install git+https://github.com/ML-EDM/ml_edm\r\n# This Fork:\r\npip install git+https://github.com/Faiber09/ML_EDM_Multi\r\n```\r\n## What’s New in This Fork\r\n\r\n- **Multivariate Time Series Support**  \r\n  This version extends the original `ml_edm` package to handle **multivariate** time series. The upstream toolkit only supported univariate data.\r\n\r\n- **AEON 1.0.0 Compatibility**  \r\n  We’ve applied minor adjustments so that `ml_edm` now works seamlessly with **AEON ≥ 1.0.0** (the original release targeted AEON 0.4.0).\r\n\r\n\r\n## Citation\r\n\r\nIf you use the original package, please refer to it using the following the bibtex entry: \r\n\r\n    @misc{renault2024mledmpackagepythontoolkit,\r\n        title={ml_edm package: a Python toolkit for Machine Learning based Early Decision Making}, \r\n        author={Aurélien Renault and Youssef Achenchabe and Édouard Bertrand and Alexis Bondu and Antoine Cornuéjols and Vincent Lemaire and Asma Dachraoui},\r\n        year={2024},\r\n        eprint={2408.12925},\r\n        archivePrefix={arXiv},\r\n        primaryClass={cs.LG},\r\n        url={https://arxiv.org/abs/2408.12925}, \r\n    }\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffaiber09%2Fml_edm_multi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffaiber09%2Fml_edm_multi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffaiber09%2Fml_edm_multi/lists"}