DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning
This tool addresses mistrust in AutoML among users by improving transparency, though it is incremental as it builds on existing optimization procedures.
The paper tackles the lack of transparency in Automated Machine Learning (AutoML) by introducing DeepCAVE, an interactive framework that provides text, tables, and visualizations to analyze optimization processes, as demonstrated in an outlier detection use-case.
Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimization process and its results due to a lack of transparency, making manual tuning still widespread. We introduce DeepCAVE, an interactive framework to analyze and monitor state-of-the-art optimization procedures for AutoML easily and ad hoc. By aiming for full and accessible transparency, DeepCAVE builds a bridge between users and AutoML and contributes to establishing trust. Our framework's modular and easy-to-extend nature provides users with automatically generated text, tables, and graphic visualizations. We show the value of DeepCAVE in an exemplary use-case of outlier detection, in which our framework makes it easy to identify problems, compare multiple runs and interpret optimization processes. The package is freely available on GitHub https://github.com/automl/DeepCAVE.