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 LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~> On Thu, Mar 29, 2018 at 7:06 AM, PARK Jinwoo <jinwoo...@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. > > <https://mail.python.org/mailman/listinfo/scikit-learn> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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