Abstract
CompareML: A Novel Approach to Supporting Preliminary Data Analysis Decision Making
Highlights
The evolution of computing capacities and the democratization of access to cloud computing at reasonable and affordable prices have fostered the appearance of a great number of machine learning applications for different fields
With the aim of answering these questions, we present CompareML, an approach that allows practitioner or research engineers to make a preliminary analysis of their data by testing different machine learning algorithms from different providers
The first case study focuses on training classification models, and provides the output metrics of each trained algorithm to allow their evaluation by users
Summary
The evolution of computing capacities and the democratization of access to cloud computing at reasonable and affordable prices have fostered the appearance of a great number of machine learning applications for different fields. Any thorough evaluation of the different algorithms and their different implementations will be a time-consuming task This results in most practitioner engineers and researchers in the field usually relying on their own experience to select a particular platform and algorithm, so that they will probably miss others that might well better reveal the whole potential of their datasets. While there are general-purpose machine learning tools that allow different algorithms to be applied to the same dataset, to the best of our knowledge, there is no solution available that is able to quickly and compare algorithm implementations from different providers All of this led us to posit the following research questions:.
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