# AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance Source: [AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:59:05.045Z Authors: Fuad Ahmed, Rubayea Ferdows, Md Rafiqul Islam, Abu Raihan M. Kamal (1) \_\_INSTITUTION\_4\_\_ Department of Computer Science & Engineering, Islamic University of Technology, Bangladesh, (2) Department of Computer Science & Engineering, International University of Business Agriculture, Technology, Bangladesh, (3) Department of Genetics, Genomics, Informatics, The University of Tennessee Health Science Center, United States) Published: 2022-02-24T07:02:37.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()