What if we just mention that it can be applied to fields like data-mining
etc.
Then it doesn't claim to be a data-mining package or library but mentions
that
it can be used/applied for that.
Unless we drop 'Data mining' alltogether from there.
2012/11/15 Nelle Varoquaux <[email protected]>
>
>
>
> On 15 November 2012 12:35, Mathieu Blondel <[email protected]> wrote:
>
>>
>>
>> On Thu, Nov 15, 2012 at 8:21 PM, Lars Buitinck <[email protected]>wrote:
>>
>>> 2012/11/15 Gael Varoquaux <[email protected]>:
>>> > scikit-learn integrates machine learning algorithms in the tightly-knit
>>> > scientific Python world, building upon numpy, scipy, and matplotlib. It
>>> > provides simple, efficient and effective data mining solutions,
>>> > accessible to everybody and reusable in various contexts. Our aim is to
>>> > provide a machine-learning module that is a versatile tool for science
>>> > and engineering in any discipline.
>>>
>>> I'm not sure if we should put "data mining solutions" in there. For
>>> me, "data mining" carries the connotation of database integration,
>>> unsupervised, exploratory algorithms and recommender systems, while
>>> "solutions" is a bit of a business term ("reads your webserver logs,
>>> visualizes them and magically suggests advertising strategies").
>>>
>>
>> +1
>>
>
> Maybe I don't use the term "data-mining" correctly, but I don't feel like
> scikit-learn is a data mining package. It's statistical learning: many
> people I know work on prediction, and do not attempt to discover patterns
> in large datasets.
>
>
>>
>>
>>>
>>> May I suggest instead of the final two sentences:
>>>
>>> "It is a Python library [or toolkit?] offering efficient
>>> implementations of common and advanced algorithms through a consistent
>>> API, reusable for various tasks in science and engineering."
>>>
>>> As for classic vs. recent -- I'm happy if both were put in, though I'd
>>> prefer "state of the art" to "recent".
>>>
>>
>> "well-known algorithms" would do the trick too.
>>
>> Mathieu
>>
>>
>>
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