MODEL-AGNOSTIC INTERPRETATION FRAMEWORK IN MACHINE LEARNING - A COMPARATIVE STUDY IN NBA SPORTS

Aug 4, 2026

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Machine learning models have advanced rapidly, yet deep architectures often function as opaque “black boxes,” reducing interpretability and limiting transparent understanding of decision rationale. In sensitive settings such as healthcare or finance, this lack of clarity hinders adoption. A proposed model-agnostic framework reconciles performance and interpretability through modular operations on high-dimensional data, combining multiple interpretability techniques while enabling end-to-end processing. Extensive testing validates improved balance between computational efficiency and interpretability, supporting trust, transparency, and accountability in deployment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Introduction

Motivation and challenges in interpretability

Related work on sports statistics and predictive modeling

Proposed framework and contributions

arXiv :2401 .02630v 1 [ cs .LG] 5 Jan 2024 MODEL-AGNOSTIC INTERPRETATION FRAMEWORK IN MACHINE LEARNING: A COMPARATIVE STUDY IN NBA SPORTS Shun Liu∗ School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai [kevinliuleo@gmail.com](kevinliuleo@gmail.com) ABSTRACT The field of machine learning has seen tremendous progress in recent years, with deep learning models delivering exceptional performance across a range of tasks. However, these models often come at the cost of interpretability, as they operate as opaque "black boxes" that obscure the rationale behind their decisions. This lack of transparency can limit understanding of the models’ underlying principles and impede their deployment in sensitive domains, such as healthcare or finance. To address this challenge, our research team has proposed an innovative framework designed to reconcile the trade-off between model performance and interpretability. Our approach is centered around modular operations on high-dimensional data, which enable end-to-end processing while preserving interpretability. By fusing diverse interpretability techniques and modularized data processing, our framework sheds light on the decision-making processes of complex models without compromising their performance. We have extensively tested our framework and validated its superior efficacy in achieving a harmonious balance between computational efficiency and interpretability. Our approach addresses a critical need in contemporary machine learning applications by providing unprecedented insights into the inner workings of complex models, fostering trust, transparency, and accountability in their deployment across diverse domains. 1 Introduction In recent years, the field of sports analytics has experienced a significant transformation with the availability of highdimensional data. This wealth of data promises to provide profound insights into athlete performance and strategic dynamics in sports. However, one major challenge that persists is the interpretability of complex statistical models used in sports analytics. Previous studies have made notable contributions to understanding high-dimensional sports statistics. For example,[1] focuses on identifying players’ performance profiles throughout regular and playoff seasons using a combination of tracking and notation variables. By employing clustering algorithms, they categorize players into different clusters based on their performance characteristics. However, this approach may overlook the influence of team quality, as players from powerful teams tend to have higher performance measures. Additionally, [2] employs machine learning and deep learning techniques to predict player performance. They use the CRISP-DM methodology and identify the top relevant features in their regression model. On the other hand,[3] develops a mini-batch optimized neural network to predict the Most Valuable Player (MVP) . Their study successfully incorporates effective predictors into the model, achieving high prediction probabilities. Despite these advancements, there are still significant challenges in interpreting these sophisticated models. Advanced algorithms, such as neural networks and ensemble methods, often lack transparency and interpretability, as discussed in [4, 5, 6, 7, 8, 9] . The complex nature of these models hinders our ability to understand the underlying decision-making processes. Moreover, the expansive feature space in high-dimensional datasets adds another layer of complexity. Feature redundancy and collinearity make it difficult to identify the most influential variables, as highlighted by [10] . Furthermore, the dynamic and context-dependent nature of sports introduces additional challenges. While studies Running Title for Header addressing interpretability in image classification and face recognition exist [11, 12, 13], there is a scarcity of research specifically focusing on th

FAQs

Why is interpretability a problem in high-dimensional sports analytics models?

Complex statistical models, especially neural networks and ensembles, often lack transparency. Large feature spaces also introduce redundancy and collinearity, making influential variables harder to identify.

What is the core idea of the proposed model-agnostic interpretation framework?

The framework applies modular operations on high-dimensional data to keep end-to-end processing while preserving interpretability. It fuses diverse interpretability techniques to clarify decision-making without sacrificing performance.

How does the study validate the effectiveness of its framework?

The research team extensively tests the framework and reports validation of superior efficacy. Results focus on achieving a harmonious balance between computational efficiency and interpretability.

Aug 4, 2026

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