# DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning

arXiv: https://arxiv.org/abs/2512.01810 · HTML: https://mlsanity.com/paper/2512.01810/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 19 of 19)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020](https://mlsanity.com/paper/2104.10201/) (2021)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020)
- [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://mlsanity.com/paper/2204.11051/) (2022)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [Explaining Hyperparameter Optimization via Partial Dependence Plots](https://mlsanity.com/paper/2111.04820/) (2021)
- [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://mlsanity.com/paper/2306.12370/) (2023)
- [Boxer: Interactive Comparison of Classifier Results](https://mlsanity.com/paper/2004.07964/) (2020)
- [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://mlsanity.com/paper/1902.05009/) (2019)
- [IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics](https://mlsanity.com/paper/2007.03953/) (2020)
- [PipelineProfiler: A Visual Analytics Tool for the Exploration of AutoML Pipelines](https://mlsanity.com/paper/2005.00160/) (2020)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Neural Architecture Search for Dense Prediction Tasks in Computer Vision](https://mlsanity.com/paper/2202.07242/) (2022)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)

## Built on by (most cited first, 2 of 2)

- [Conditional PED-ANOVA: Hyperparameter Importance in Hierarchical & Dynamic Search Spaces](https://mlsanity.com/paper/2601.20800/) (2026)
- [Beyond Success Rates: Trainability and Extractability for Offline GCRL](https://mlsanity.com/paper/2602.05459/) (2026)

## Abstract (arXiv)

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.