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From Gilles Sadowski <gillese...@gmail.com>
Subject Re: [statistics-regression] Proposed Regression class/method structure
Date Tue, 22 Oct 2019 23:13:01 GMT
Hello.

Le mar. 22 oct. 2019 à 21:50, Eric Barnhill <ericbarnhill@gmail.com> a écrit :
>
> I propose the following class structure for commons-statistics-regression.

Which?
[Attachment was probably stripped: such should go to a JIRA report.]

> The interface carried over from commons-math is more of an academic approach to thinking
about regression. For rebooting the library (and I hinted at this when I wrote the tickets
for summer of code) I was hoping to emulate widespread tools like R and scikit-learn, and
consider that "machine learning" is an increasingly popular use of regression. This proposed
structure creates an interface that is not the same as, but will be very friendly to, anyone
coming from R or scikit-learn, or similar tools in JavaScript.
>
> There are of course many ways I can see to elaborate this scheme, say using RegressionResult
objects and so forth. But Matrices paired with a double[], returning a double[] of coefficients
or predictions, are likely to be the most common use cases and should be plenty to get started.

Commenting perhaps too early (not seeing the proposed design), but we broadly
discussed that the linear algebra API is not easy to get right, and once we "get
started", the trend is to be stuck with it for ages (related issues
are among the
oldest unresolved ones in CM).

> Under the hood I would use the available implementations in commons-math to get up and
running, and worry about improving them later.

Do you mean port from, or depend on, CM?

Regards,
Gilles

>
> Feedback appreciated,
> Eric

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