Update/ Install Python Scikit Learn Latest Development Version In Windows

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Open source scripts, reports, and preprints for in vitro biology, genetics, bioinformatics, crispr, and other biotech applications.Open source scripts, reports, and preprints for in vitro biology, genetics, bioinformatics, crispr, and other biotech applications.Search for:CategoriesCategoriesCategoriesUpdate/ install Python scikit learn latest development version in WindowsI am trying to replace/install my Python scikit-learn 0.17.0 with 0.18.dev0 latest development version in Windows, so that I can try out sklearn.neural_network.MLPClassifier. After reading and trying the advanced user manual here (scikit-learn.org/stable/developers/advanced_installation.html#install-bleeding-edge ), I am kinda stuck.pip install -U scikit-learn => Still getting 0.17.0 but not 0.18.dev0Could any guru provide step by step guides to install developmental Python 2.70 scikit-learn in windows? Thanks!Answer #1:Answer #1:The only way to get the development version is to clone it through git, as follows:pip install git+https://github.com/scikit-learn/scikit-learn.gitFor Windows, I highly recommend installing Anaconda (www.continuum.io/downloads ) because it installs all the scientific computing packages that you need for Windows.Answer #2:I have same problem with you. After trying several solutions, I finally get sklearn 0.18 worked by simply type:pip install git+https://github.com/scikit-learn/scikit-learn.gitAnswer #2:I have same problem with you. After trying several solutions, I finally get sklearn 0.18 worked by simply type:pip install git+https://github.com/scikit-learn/scikit-learn.gitAnswer #3:conda install scikit-learnseems to do the job of updating to the latest version.you can find the package you are looking for there:from sklearn.neural_network import MLPClassifierAnswer #3:conda install scikit-learnseems to do the job of updating to the latest version.you can find the package you are looking for there:from sklearn.neural_network import MLPClassifierThis is valid if you are using Anaconda, ofc.
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