electronics Article Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model Mudabbir Ali 1 , Asad Masood Khattak 2 , Zain Ali 3 , Bashir Hayat 4 , Muhammad Idrees 5 , Zeeshan Pervez 6 , Kashif Rizwan 7 , Tae-Eung Sung 8 and Ki-Il Kim 9, *   Citation: Ali, M.; Khattak, A.M.; Ali, Z.; Hayat, B.; Idrees, M.; Pervez, Z.; Rizwan, K.; Sung, T.-E.; Kim, K.-I. Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model. Electronics 2021, 10, 3045. https:// doi.org/10.3390/electronics10233045 Academic Editors: Maja Matetic, Xiaoshuan Zhang and Marija Brki´ c Bakari´ c Received: 15 October 2021 Accepted: 3 December 2021 Published: 6 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan; mudabbirali92@yahoo.com 2 College of Technological Innovation, Zayed University, Abu Dhabi 19282, United Arab Emirates; Asad.Khattak@zu.ac.ae 3 Department of Electrical Engineering, HITEC University, Taxila 47080, Pakistan; Zain29047@gmail.com 4 Institute of Management Sciences Peshawar, Peshawar 25100, Pakistan; bashir.hayat@imsciences.edu.pk 5 Department of Computer Science and Engineering, University of Engineering and Technology, Norowal Campus, Lahore 54890, Pakistan; midrees10@uet.edu.pk 6 School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, UK; zeeshan.pervez@uws.ac.uk 7 Department of Computer Science, Federal Urdu University of Arts, Science and Technology, Islamabad 44000, Pakistan; kashifrizwan@fuuast.edu.pk 8 Department of Software, Yonsei University, Wonju 26493, Korea; tesung@yonsei.ac.kr 9 Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea * Correspondence: kikim@cnu.ac.kr Abstract: Machine learning has the potential to predict unseen data and thus improve the productivity and processes of daily life activities. Notwithstanding its adaptiveness, several sensitive applications based on such technology cannot compromise our trust in them; thus, highly accurate machine learning models require reason. Such models are black boxes for end-users. Therefore, the concept of interpretability plays the role if assisting users in a couple of ways. Interpretable models are models that possess the quality of explaining predictions. Different strategies have been proposed for the aforementioned concept but some of these require an excessive amount of effort, lack generalization, are not agnostic and are computationally expensive. Thus, in this work, we propose a strategy that can tackle the aforementioned issues. A surrogate model assisted us in building interpretable models. Moreover, it helped us achieve results with accuracy close to that of the black box model but with less processing time. Thus, the proposed technique is computationally cheaper than traditional methods. The significance of such a novel technique is that data science developers will not have to perform strenuous hands-on activities to undertake feature engineering tasks and end-users will have the graphical-based explanation of complex models in a comprehensive way—consequently building trust in a machine. Keywords: machine learning; surrogate models; signal processing; data science; interpretable model; supervised learning 1. Introduction Machine learning, a substitute for artificial intelligence, is a pivotal part of modern computer society. It involves scientific and algorithmic techniques that are specifically designed to curb user involvement and thus increase the automaticity of the desired work. Nowadays, most computer systems are built for performing general or business- specific tasks through machine learning technologies. Evidence of their applications can be seen in problem domains such as in the medical field, policy-making [1], fraud detection [2–4], and signal processing [5,6]. Different machine learning models, also known as mathematical models, possess different attributes that produce accurate results using Electronics 2021, 10, 3045. https://doi.org/10.3390/electronics10233045 https://www.mdpi.com/journal/electronics