# AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance

arXiv: https://arxiv.org/abs/2202.11928 · HTML: https://mlsanity.com/paper/2202.11928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 24 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [Auto-Keras: An Efficient Neural Architecture Search System](https://mlsanity.com/paper/1806.10282/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Automated Machine Learning: State-of-The-Art and Open Challenges](https://mlsanity.com/paper/1906.02287/) (2019)

## Abstract (arXiv)

Nowadays, deep learning (DL) models being increasingly applied to various fields, people without technical expertise and domain knowledge struggle to find an appropriate model for their task. In this paper, we introduce AutoCl a visual interactive recommender system aimed at helping non-experts to adopt an appropriate DL classifier. Our system enables users to compare the performance and behavior of multiple classifiers trained with various hyperparameter setups as well as automatically recommends a best classifier with appropriate hyperparameter. We compare features of AutoCl against several recent AutoML systems and show that it helps non-experts better in choosing DL classifier. Finally, we demonstrate use cases for image classification using publicly available dataset to show the capability of our system.