Machine Learning Pipeline Optimization with TPOT

Machine Learning Pipeline Optimization with TPOT

It's been a while since I've had a look at TPOT, the Tree-based Pipeline Optimization Tool. TPOT is a Python automated machine learning (AutoML) tool for optimizing machine learning pipelines through the use of genetic programming. We are told by the authors to consider it our "data science assistant."

It's been a while since I've had a look at TPOT, the Tree-based Pipeline Optimization Tool. TPOT is a Python automated machine learning (AutoML) tool for optimizing machine learning pipelines through the use of genetic programming. We are told by the authors to consider it our "data science assistant."

The rationale for AutoML stems from this idea: if numerous machine learning models must be built, using a variety of algorithms and a number of differing hyperparameter configurations, then this model building can be automated, as can the comparison of model performance and accuracy.

I want to have a fresh look at TPOT to to see if we can flesh out an actual fully-automated assistant for data scientists. What if we could expand on the functionality of TPOT and build an end-to-end prediction pipeline, which we could point at a dataset and get predictions out the other end, with no intervention in between? Sure, other possible tools for this exist, but what better way to understand the machine learning pipeline process, and any particular resulting single constructed pipeline, than building it ourselves, and making the decisions as to what happens along the way.

The goal wouldn't necessarily be to cut the data scientist out of the loop altogether, but to provide a baseline or a number of possible solutions to compare hand-crafted machine learning pipelines to. While the assistant toils in the background, the master can come up with more clever attempted approaches. At the very least, resulting prediction pipelines could be good starting points for a data scientist to manually tweak and intervene with after the fact, with much of the rote work taken care of on her behalf.

An AutoML "solution" could include the tasks of data preprocessing, feature engineering, algorithm selection, algorithm architecture search, and hyperparameter tuning, or some subset or variation of these distinct tasks. Thus, automated machine learning can now be thought of as anything from solely performing a single task, such as automated feature engineering, all the way through to a fully-automated pipeline, from data preprocessing, to feature engineering, to algorithm selection, and so on. So why not build something that does it all?

Anyhow, the first step of this plan is to refamiliarize ourselves with TPOT, the project that will eventually be at the center of our fully-automated prediction pipeline optimizer. TPOT is a Python tool which "automatically creates and optimizes machine learning pipelines using genetic programming." TPOT works in tandem with Scikit-learn, describing itself as a Scikit-learn wrapper. TPOT is open source, written in Python, and aimed at simplifying a machine learning process by way of an AutoML approach based on genetic programming. The end result is automated hyperparameter selection, modeling with a variety of algorithms, and exploration of numerous feature representations, all leading to iterative model building and model evaluation.

automl machine learning optimization pipeline

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