> However, these are typically considered "machine learning" techniques; when someone says "AI", they typically mean a Neural Network.
I am sorry but I disagree: https://en.wikipedia.org/wiki/Artificial_intelligence On 29 March 2018 at 06:47, Andrew Howe <ahow...@gmail.com> wrote: > 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 >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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