Python Tpot offers automated machine learning.
Click to access olson_tpot_2016.pdf
Machine learning is commonly described as a field of study that gives computers the ability to learn without being explicitly programmed”(Simon, 2013). Despite this common claim, machine learning practitioners know that designing effective machine learning pipelines is often a tedious endeavour, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish (Olson et al., 2016a). Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires considerable explicit programming. In response to this challenge, several automated machine learning methods have been developed over the years (Hutter et al., 2015).Over the past year, we have been developing a Tree-based Pipeline Optimization Tool (TPOT) that automatically designs and optimizes machine learning pipelines for a given problem domain (Olson et al., 2016b), without any need for human intervention. In short, TPOT optimizes machine learning pipelines using a version of genetic programming (GP)
TPOT is built on top of scikit-learn, so all of the code it generates should look familiar… if you’re familiar with scikit-learn, anyway.
Citation: Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, and Jason H. Moore (2016). Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. Proceedings of GECCO 2016, pages 485-492