AllMetrics: A Unified Python Library for Standardized Metric Evaluation and Robust Data Validation in Machine Learning

2025-05-21

AI Summary

Existing ML evaluation metric libraries suffer from fragmentation, inconsistent implementations, and weak data validation, hindering reliable cross-framework comparisons. To address this, we propose AllMetrics—the first task-aware, standardized metric framework supporting diverse tasks including regression, classification, clustering, segmentation, and image-to-image translation. AllMetrics systematically eliminates implementation discrepancies (IDs) and reporting discrepancies (RDs) via a modular API, strongly typed input validation, multi-task adapters, and cross-language (Python/Matlab/R) consistency verification. Empirical evaluation across healthcare, finance, and real estate domains demonstrates that AllMetrics significantly reduces evaluation errors, improves reproducibility, and enhances trustworthiness in ML workflows.

Technology Category

• Machine Learning: Evaluation and Analysis
• Computer Vision: Multi-modal Vision
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Machine learning (ML) models rely heavily on consistent and accurate performance metrics to evaluate and compare their effectiveness. However, existing libraries often suffer from fragmentation, inconsistent implementations, and insufficient data validation protocols, leading to unreliable results. Existing libraries have often been developed independently and without adherence to a unified standard, particularly concerning the specific tasks they aim to support. As a result, each library tends to adopt its conventions for metric computation, input/output formatting, error handling, and data validation protocols. This lack of standardization leads to both implementation differences (ID) and reporting differences (RD), making it difficult to compare results across frameworks or ensure reliable evaluations. To address these issues, we introduce AllMetrics, an open-source unified Python library designed to standardize metric evaluation across diverse ML tasks, including regression, classification, clustering, segmentation, and image-to-image translation. The library implements class-specific reporting for multi-class tasks through configurable parameters to cover all use cases, while incorporating task-specific parameters to resolve metric computation discrepancies across implementations. Various datasets from domains like healthcare, finance, and real estate were applied to our library and compared with Python, Matlab, and R components to identify which yield similar results. AllMetrics combines a modular Application Programming Interface (API) with robust input validation mechanisms to ensure reproducibility and reliability in model evaluation. This paper presents the design principles, architectural components, and empirical analyses demonstrating the ability to mitigate evaluation errors and to enhance the trustworthiness of ML workflows.

Problem

• Fragmented and inconsistent ML metric implementations across libraries
• Lack of standardized data validation in performance evaluation
• Difficulty comparing results due to varying computation methods

Innovation

• Unified Python library for standardized ML metrics
• Modular API with robust input validation
• Task-specific parameters to resolve discrepancies