Lale: Consistent Automated Machine Learning
This work addresses usability issues for data scientists by offering a more consistent and feature-rich automated machine learning tool, though it is incremental as it builds on existing tools.
The paper tackles the inconsistency and limited features in automated machine learning tools by introducing Lale, a Python library that provides high-level interfaces to unify and simplify the process, enabling consistent syntax and support for advanced features like topology search.
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.