As the popularity of machine learning techniques spreads to new areas of industry and science, the number of potential machine learning users is growing rapidly. While the fantastic scikit-learn library is widely used in the Python community for tackling such tasks, there are two significant hurdles in place for people working on new machine learning problems:
• Scikit-learn requires writing a fair amount of boilerplate code to run even simple experiments.
• Obtaining good performance typically requires tuning various model parameters, which can be particularly challenging for beginners.
SciKit-Learn Laboratory (SKLL) is an open source Python package, originally developed by the NLP & Speech group at the Educational Testing Service (ETS), that addresses these issues by providing the ability to run scikit-learn experiments with tuned models without writing any code beyond what generates the features. This talk will provide an overview of performing common machine learning tasks with SKLL, and highlight some of the new features that are present as of the 1.0 release.