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A few machine learning packages implement semi-automated model hyper-parameter selection.
Some of them are implemented in various machine learning packages, but many are not.
AZOrange complements already available machine learning packages by interfacing and customizing several state-of-the-art machine learning algorithms.
In the field of education, IBM envisages teachers being able to tap into Watson to identify individual pupil's needs and offer bespoke learning packages for each child.
Despite the diversity of available machine learning packages, there is no package fulfilling all of the requirements on an Open Source state-of-the-art QSAR modeling platform.
Computer aided learning packages play an important role in this process, as they enable students to explore and gain experience of new software environments in subject specific context.
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Calling the algorithms was done using the Scikit-learn machine learning package [79, 80].
This paper describes the implementation of the Open Source machine learning package AZOrange.
The dialogic dimensions of a hypermedia learning package are often perceived as the interactivity of its design.
This research adopts the quantitative approach and the ADDIE model is used in the development of this multimedia learning package.
We employed information gain attribute evaluator, relief attribute evaluator, and correlation-based feature selection (CFS) from Weka machine learning package [62] for the gene selection.
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