pyclustering 0.8.1

Новиков, А. В.

doi:10.5281/zenodo.1254845

Cited by 1 publication

(1 citation statement)

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“…Tasks not available in these packages were included from other sources (e.g. imblearn [6], pyclustering [7]) and adapted to fit the generic structure of scikit-learn and SciPy.…”

Section: Propheticus Frameworkmentioning

confidence: 99%

Propheticus: Generalizable Machine Learning Framework

Campos¹,

Vieira²,

Costa³

2018

Preprint

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Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing the logic required to execute the experiments is no small nor trivial deed, consequentially increasing the probability of faulty code which can compromise the results. Propheticus is a data-driven framework which results of the need for a tool that abstracts some of the inherent complexity of ML, whilst being easy to understand and use, as well as to adapt and expand to assist the user's specific needs. Propheticus systematizes and enforces various complex concepts of an ML experiment workflow, taking into account the nature of both the problem and the data. It contains functionalities to execute all the different tasks, from data preprocessing, to results analysis and comparison. Notwithstanding, it can be fairly easily adapted to different problems due to its flexible architecture, and customized as needed to address the user's needs.

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“…Tasks not available in these packages were included from other sources (e.g. imblearn [6], pyclustering [7]) and adapted to fit the generic structure of scikit-learn and SciPy.…”

Section: Propheticus Frameworkmentioning

confidence: 99%