Automatic Machine Learning (AML) is a pipeline, which enables you to automate the repetitive steps in your Machine Learning (ML) problems and so save time to focus on parts where your expertise has higher value. What is great is that it is not only some vague idea, but there are applied packages, which build on standard python ML packages such as scikit-learn.
Anyone familiar with Machine Learning will in this context most probably recall the term grid search. And they will be entirely right to do so. AML is in fact an extension of grid search, as applied in scikit-learn, however instead of iterating over a predefined set of values and their combinations it searches for optimal solutions across methods, features, transformations and parameter values. AML “grid search” therefore does not have to be an exhaustive search over the space of possible configurations – one great application of AML is package called TPOT, which offers applications of e.g. genetic algorithms to mix the individual parameters within a configuration and arrive at the optimal setting.
In this post I will shortly present some basics of AML and then dive into applications using TPOT package including its genetic algorithm solution optimization.
The basic concept is very simple, once we receive our raw data we start with the standard ML pipeline.