Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model

Author First name, Last name, Institution

Mudabbir Ali, COMSATS University Islamabad
Asad Masood Khattak, Zayed University
Zain Ali, HITEC University
Bashir Hayat, Institute of Management Sciences Peshawar
Muhammad Idrees, University of Engineering and Technology, Lahore
Zeeshan Pervez, University of the West of Scotland
Kashif Rizwan, Federal Urdu University of Arts
Tae Eung Sung, Yonsei University Mirae Campus
Ki Il Kim, Chungnam National University

ORCID Identifiers

0000-0002-0630-1264

0000-0002-5576-2278

0000-0002-3618-779X

0000-0002-5595-3867

0000-0003-2763-5332

0000-0002-8366-3533

Document Type

Article

Source of Publication

Electronics (Switzerland)

Publication Date

12-1-2021

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.

DOI Link

10.3390/electronics10233045

ISSN

2079-9292

Publisher

MDPI AG

Volume

10

Issue

23

Disciplines

Computer Sciences

Keywords

Data science, Interpretable model, Machine learning, Signal processing, Supervised learning, Surrogate models

Scopus ID

85120685537

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Ali, Mudabbir; Khattak, Asad Masood; Ali, Zain; Hayat, Bashir; Idrees, Muhammad; Pervez, Zeeshan; Rizwan, Kashif; Sung, Tae Eung; and Kim, Ki Il, "Estimation and Interpretation of Machine Learning Models with Customized Surrogate Model" (2021). All Works. 4725.
https://zuscholars.zu.ac.ae/works/4725

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series