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Auto-Sklearn: How To Boost Performance and Efficiency Through Automated Machine Learning
Learn how to leverage AutoML to maximize the outcome of your machine learning workflows
Many of us are familiar with the challenge of selecting a suitable machine learning model for a specific prediction task, given the vast number of models to choose from. On top of that, we also need to find optimal hyperparameters in order to maximize our model’s performance.
These challenges can largely be overcome through automated machine learning, or AutoML. I say largely because, despite its name, the process is not fully automated and still requires some manual tweaking and decision-making by the user.
Essentially, AutoML frees the user from the daunting and time-consuming tasks of data preprocessing, model selection, hyperparameter optimization, and ensemble building. As a result, this toolkit not only saves precious time for the experts, but also enables non-technical users to break into the field of machine learning. In the words of the authors:
Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.
