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https://issues.apache.org/jira/browse/STATISTICS-7?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16805264#comment-16805264
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Udit Arora commented on STATISTICS-7:
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Sir Eric Barnhilll and Sir Gilles
Do we intend to include functions that help aid calculations in machine
learning. We can add somethings such that given a data set we could give the
number of parameters that would give a classifier decent enough so that loss is
not minimized but still is a significantly low number? Since I am new to
machine learning I am not aware if there already exists a function as that.
Thanks
> Stream-based Java statistical processing
> ----------------------------------------
>
> Key: STATISTICS-7
> URL: https://issues.apache.org/jira/browse/STATISTICS-7
> Project: Apache Commons Statistics
> Issue Type: New Feature
> Reporter: Eric Barnhill
> Priority: Major
> Labels: GSoC2019, gsoc2019, statistics, streams
>
> The new component aims to be a library of commons statistics functions
> synchronized with the latest developments in the Java language, in particular
> Java's functional programming syntax.
> The library will make commonly used statistical functions available to an end
> user through a simple grammar comparable to commons-math-statistics or
> scikit-learn, while under the hood will implement Java's mapping, streaming,
> and other producer and consumer functions to ensure the statistical methods
> run optimally in new Java implementations.
> Developers working on the project will have the opportunity to demonstrate
> Java programming, functional programming, algorithm design, and data science
> skills and receive authorship on a commons project that is likely to be
> widely used.
> The ideal contributor will also be able to help with important architectural
> decision making. The old source of these libraries, commons-math, grew too
> large, hierarchically complex and interdependent for the commons mission. The
> developers on this project need to make architectural choices that will
> enable the statiscal code to be lightweight and reusable, with a minimum of
> outside dependencies while avoiding redundancy.
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