ml_edm package: a Python toolkit for Machine Learning based Early Decision Making

TL;DR

The ml_edm Python library provides a modular, efficient toolkit for early decision making in sequential data tasks, supporting various algorithms and easy integration with scikit-learn pipelines.

Contribution

It introduces a comprehensive, parallelized implementation of state-of-the-art early classification algorithms with a flexible, user-friendly API for researchers.

Findings

Efficient implementation of multiple ECTS algorithms.

Compatibility with scikit-learn pipelines.

Open-source with modular design for customization.

Abstract

\texttt{ml\_edm} is a Python 3 library, designed for early decision making of any learning tasks involving temporal/sequential data. The package is also modular, providing researchers an easy way to implement their own triggering strategy for classification, regression or any machine learning task. As of now, many Early Classification of Time Series (ECTS) state-of-the-art algorithms, are efficiently implemented in the library leveraging parallel computation. The syntax follows the one introduce in \texttt{scikit-learn}, making estimators and pipelines compatible with \texttt{ml\_edm}. This software is distributed over the BSD-3-Clause license, source code can be found at \url{https://github.com/ML-EDM/ml_edm}.