[scikit-learn] (no subject)

[Andreas Mueller]

Try this:
https://jakevdp.github.io/PythonDataScienceHandbook/

On 03/28/2018 11:49 PM, PARK Jinwoo wrote:
> Dear scikit-learn experts
>
> Hello, I am a graduate school student majoring in doping control
> analysis in Korea.
> Now I'm in a research institute that carries out doping control analyses.
>
> I received a project by my advising doctor. It's about operating an AI project.
> A workshop is scheduled in April, so it needs to be done in a month.
> However, I haven't learn computer science at all and I'm totally ignorant of it.
> So I desperately need your advice.
>
> To be specific, the 3 xml files shown in the picture are analysis results
> named positive, negative, and unknown from top to bottom.
> We'd like to let AI learn positive and negative data,
> input unknown datum, and then see what result will turn out.
>
> I came to know that there's a module called 'iris calssification' in
> scikit-learn
> and I'm thinking of utilizing that as it seems similar with my assignment
> However, while the database of iris is a csv file with 150 data and
> labels inside,
> what I have are 3 xml files each one of which represents one data,
> which are stored in C:\Users\Jinwoo\Documents\Python Scripts\mzdata
> The training process is not shuffling randomly the 150 data and
> dividing into training set and test set. The data are already assigned
> into training ones and testing one.
> Also, when training the program, training labels naming positive and
> negative should be inserted on my own.
>
> What I know all is that it will be appropriate to use fit() function
> and predict() function to train and test.
> But I have no idea on what to import, how to write codes correctly, and so on
>
> It will be thankful to give me some help
>
>
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