[scikit-learn] I’m in trouble and I need your advice on operating scikit-learn

[Andrew Howe]

Hi Jinwoo

It is true that scikit-learn has many models for supervised classification
tasks, and it should be relatively trivial for you to munge your 3 data
files into the X (data) y (labels) format required for these methods.
Examples are k-means, Support Vector Machines, Decision Trees, and
Discriminant Analysis.  However, these are typically considered "machine
learning" techniques; when someone says "AI", they typically mean a Neural
Network.  If you wish to use scikit-learn for Neural Network
classification, you are limited to the Multilayer Perceptron:
http://scikit-learn.org/stable/modules/neural_networks_supervised.html#.
If you want to be able to use more advanced Neural Networks, here are some
options:

*Deep neural networks etc.*

   - pylearn2 <http://deeplearning.net/software/pylearn2/> A deep learning
   and neural network library build on theano with scikit-learn like interface.
   - sklearn_theano <http://sklearn-theano.github.io/> scikit-learn
   compatible estimators, transformers, and datasets which use Theano
   internally
   - nolearn <https://github.com/dnouri/nolearn> A number of wrappers and
   abstractions around existing neural network libraries
   - keras <https://github.com/fchollet/keras> Deep Learning library
   capable of running on top of either TensorFlow or Theano.
   - lasagne <https://github.com/Lasagne/Lasagne> A lightweight library to
   build and train neural networks in Theano.

I personally use Google's TensorFlow.  Hope this helps.

Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
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On Thu, Mar 29, 2018 at 7:06 AM, PARK Jinwoo <jinwoo412 at gmail.com> 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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