DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning

TL;DR

DeepCAVE is an interactive visualization tool designed to help users understand, analyze, and debug hyperparameter optimization in AutoML, thereby improving interpretability and fostering better ML model tuning.

Contribution

It introduces an interactive dashboard for visualizing and analyzing HPO processes, enhancing interpretability and debugging capabilities in AutoML workflows.

Findings

Enables exploration of HPO process details

Identifies issues and untapped potentials in ML models

Supports development of more robust AutoML methods

Abstract

Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing insights into HPO. Through an interactive dashboard, researchers, data scientists, and ML engineers can explore various aspects of the HPO process and identify issues, untouched potentials, and new insights about the ML model being tuned. By empowering users with actionable insights, DeepCAVE contributes to the interpretability of HPO and ML on a design level and aims to foster the development of more robust and efficient methodologies in the future.