# DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning

arXiv: https://arxiv.org/abs/2206.03493 · HTML: https://mlsanity.com/paper/2206.03493/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 7 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges](https://mlsanity.com/paper/2107.05847/) (2021)
- [Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets](https://mlsanity.com/paper/1605.07079/) (2016)
- [Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence](https://mlsanity.com/paper/2002.04803/) (2020)
- [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL](https://mlsanity.com/paper/2006.13799/) (2020)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [Best Practices for Scientific Research on Neural Architecture Search](https://mlsanity.com/paper/1909.02453/) (2019)
- [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Benchmarking Discrete Optimization Heuristics with IOHprofiler](https://mlsanity.com/paper/1912.09237/) (2019)
- [Fast Gradient-Based Inference with Continuous Latent Variable Models in Auxiliary Form](https://mlsanity.com/paper/1306.0733/) (2013)

## Built on by (most cited first, 14 of 14)

- [Hyperparameters in Reinforcement Learning and How To Tune Them](https://mlsanity.com/paper/2306.01324/) (2023)
- [PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces](https://mlsanity.com/paper/2304.10255/) (2023)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Hyperparameter Importance Analysis for Multi-Objective AutoML](https://mlsanity.com/paper/2405.07640/) (2024)
- [Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning](https://mlsanity.com/paper/2309.03581/) (2023)
- [The Role of Hyperparameters in Predictive Multiplicity](https://mlsanity.com/paper/2503.13506/) (2025)
- [ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning](https://mlsanity.com/paper/2409.18827/) (2024)
- [Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization](https://mlsanity.com/paper/2511.02570/) (2025)
- [From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search](https://mlsanity.com/paper/2212.08448/) (2022)
- [Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making](https://mlsanity.com/paper/2403.12664/) (2024)
- [Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid](https://mlsanity.com/paper/2506.03817/) (2025)
- [Preference-Optimal Multi-Metric Weighting for Parallel Coordinate Plots](https://mlsanity.com/paper/2507.02905/) (2025)
- [Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network](https://mlsanity.com/paper/2512.16491/) (2025)
- [Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks](https://mlsanity.com/paper/2606.23208/) (2026)

## Abstract (arXiv)

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.