Lale: Consistent Automated Machine Learning

Abstract

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.

KDD Workshop on Automation in Machine Learning (AutoML@KDD), August 2020

References in corpus (2)

• Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
• Mining Documentation to Extract Hyperparameter Schemas

Cited by in corpus (1)

• Mining Documentation to Extract Hyperparameter Schemas