Yesterday, I was looking for some examples for Machine Learning algorithms in Python; and I found a amazing package called scikit-learn, which is described on its own site:
“ scikit-learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.”
But, I found a minor issue: all guides for the installation, are for Debian/Ubuntu, MacPorts, NetBSD, but not for my Linux distribution: Fedora. So, I said: “ OK, let me try to create a simple How-To to installing this package on Fedora”.
Step 1: Dependencies
First, we need to install all dependencies for it:
# yum update && yum install scipy.x86_64 numpy.x86_64 python-devel.x86_64 python-matplotlib.x86_64 python-pip.noarch gcc-c++.x86_64
Step 2: Installing scikit-learn using pip
Now, I will use the fastest way to install the package using pip:
# pip-python install scikit-learn
This command should give a output very similar to this:
# warning: no files found matching ‘test.py’ # warning: no files found matching ‘*.TXT’ under directory ‘sklearn/datasets’ # Installing /usr/lib64/python2.7/site-packages/scikit_learn-0.9-py2.7- # Successfully installed scikit-learn Cleaning up…
Step 3: Runing the examples
The final step is to run several examples provided here
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