An Introduction to Scikit-Learn

January 16, 2014 presentation at the Research Triangle Analysts IPython notebook is available here: http://nbviewer.ipython.org/github/tdhopper/Research-Triangle-Analysts--Intro-to-scikit-learn/blob/master/Intro%20to%20Scikit-Learn.ipynb Abstract: Scikit-learn is an actively developed Python package providing an implementation of many machine learning algorithms (e.g. SVM, kNN, linear models, HMM, k-Means, spectral clustering). However, the benefits of Scikit-learn goes well beyond carefully implemented learning algorithms. Being built in Python, it allows easy integration with countless other Python modules for tasks such as plotting, data munging, and application development. Its consistent API across algorithms allows for rapid experimentation with multiple learning methods. Also, Scikit-learn is well documented and provides lots of examples. Instead of discussing particular machine learning algorithms provided by the package, I will focus on Scikit-learn and Python as a toolkit for solving data problems from start to finish. I will emphasize the Pipeline tool which allows the user to chain together all the steps of a machine learning pipeline including preprocessing, dimensionality reduction, feature selection, and model fitting.