tpot

Have you seen TPOT [0]? AutoML library that uses genetic algorithms to write scikit code for you. So fun. [0] https://github.com/EpistasisLab/tpot

There is a Python library called TPOT that does this. https://github.com/EpistasisLab/tpot

TPOT is also a good automl package that utilizes sklearn in Python. It's also actively under development. (I'm not affiliated, just an occasional user) https://github.com/rhiever/tpot

There exists a tool called TPOT (Tree-based Pipeline Optimization Tool) [0] that aims to automate the knob-twiddling that tends to go with optimizing Machine Learning models. As these models often have a number of parameters to tune and tweak over large scales, such a tool can be useful to identify performant combinations of these parameters and save time in doing so. However, many ML practitioners are wary of similar automated ML pipelines, especially as they focus on non-expert users. A huge part of "data science" is the "data" itself. It often has idiosyncrasies and quirks that must be identified and accounted for in any model that hopes to make useful predictions. There are many pitfalls that come from not understanding the base statistical/mathematical assumptions of these tools, and a simplified Automatic ML Suite runs the risk o

There's a key statement here that most will either miss or never reach. The second last paragraph > Machine Learning Automation Tuning models is a pain, and if you don't understand the model and all its parameters (like most software engineers who's job is the write software and not build models!) it takes time and can be immensely frustrating. Randal Olson[1] just announced TPOT[2], a Python tool that "automatically creates and optimized machine learning pipelines using genetic programming". This is going to be a huge lever for engineers wanting to experiment/implement with ML algorithms. [1] http://www.randalolson.com/2016/05/08/tpot-a-python-tool-for... [2] https://github.com/rhiever/tpot