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>
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