Source-linked AI summary
Insights into Performance Fitness and Error Metrics for Machine Learning
M. Z. Naser, Amir Alavi
TL;DR
The paper addresses how to determine whether ML models adequately describe investigated phenomena, particularly amid limited systematic guidance on metric selection. It compiles commonly used performance fitness and error metrics for regression and classification, reviews their fundamentals and limitations, and recommends multi-fitness validation while retaining a broad scope across problems and data quality.
Problem
The paper asks how ML models can be evaluated for accuracy, usefulness, and proper validation when systematic guidance on selecting performance metrics is limited.
Method
The paper groups widely used performance fitness and error metrics into traditional and modern categories and reviews their use for regression and classification models.
Results
The paper concludes that multi-fitness criteria should be used to support ML-model validity because individual metrics have limitations.
Takeaways & Limitations
Engineering ML applications require thorough validation using multiple performance and error metrics rather than relying on a single conventional measure.
Takeaways & Limitations
Engineering observations are often limited in size and completeness because full-scale testing, specialized equipment, and varied samples constrain data availability.
Abstract
from arXiv · showhide
Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our daily lives and operations as well as complex and interdisciplinary fields. With the rise of commercial, open-source and user-catered ML tools, a key question often arises whenever ML is applied to explore a phenomenon or a scenario: what constitutes a good ML model? Keeping in mind that a proper answer to this question depends on a variety of factors, this work presumes that a good ML model is one that optimally performs and best describes the phenomenon on hand. From this perspective, identifying proper assessment metrics to evaluate performance of ML models is not only necessary but is also warranted. As such, this paper examines a number of the most commonly-used performance fitness and error metrics for regression and classification algorithms, with emphasis on engineering applications.
1. Introduction
Machine learning is increasingly applied across disciplines because it can learn patterns from data without assuming predefined input–output relationships. This expansion makes rigorous validation and appropriate performance and error metrics essential, yet systematic guidance on metric selection remains limited.
- ML methods can derive relationships between inputs and outputs without assuming prior functional forms, enabling exploration of hidden data-pattern relationships.
- ML has expanded beyond computer science into engineering, social sciences, space applications, and other research fields.
- User-friendly software packages simplify ML through predefined algorithms and training/validation procedures, but may encourage misinterpretation or biased outcomes when data and expertise are limited.
- Researchers ask whether ML models are accurate, useful, and properly validated, and how these questions can be answered confidently.
- Performance and error metrics compare predicted observations with actual measurements to assess model adequacy and support extension to new datasets.
- This work compiles commonly used metrics and highlights their application for evaluating regression and classification ML models because systematic scenario-specific guidance is lacking.
2. Performance Fitness and Error Metrics
The paper organizes performance fitness and error metrics (PFEMs) for regression and classification, emphasizing their fundamentals, uses, recommendations, and limitations. It presents commonly used metrics while noting that metric selection depends on the evaluation scenario.
- Metric framework: PFEMs are grouped into traditional and modern categories for assessing machine-learning models.The section defines A as actual measurements, P as predictions, and n as the number of data points.
- Regression metrics: Regression metrics measure point distances using subtraction or division, with absoluteness or squareness added to the basic operations.These metrics commonly use A-P or P/A formulations.
- Regression metrics: Absolute error preserves prediction units and prevents cancellation, but it cannot reveal bias from the errors.Absolute error is non-negative and remains directly relatable to measured and predicted values.
- Regression metrics: Squared-error metrics facilitate optimization and emphasize large errors, but they are susceptible to outliers and produce squared units.Squared error also mitigates mutual cancellation of errors.
- Regression metrics: MAPE cannot be used when actual values are zero and is nonsymmetrical, with unequal behavior for overpredictions and underpredictions.Its percentage error is bounded for small predictions but has no upper limit when predictions are too high.
- Classification metrics: Classification evaluation commonly uses a confusion matrix, whose columns represent predicted instances and rows represent actual instances.The matrix summarizes actual and predicted classifications and underlies accuracy measurements for classifiers.
3. Closing Remarks
Reliable ML evaluation requires rigorous validation because limited engineering observations and good fitness metrics alone may not establish that a model represents real observations. The paper therefore emphasizes robustness, sensitivity analysis, and careful metric selection.
- Limited and incomplete engineering observations constrain confidence in ML prediction accuracy and the evaluation of model performance.Full-scale testing, specialized equipment, and varied samples contribute to this limitation.
- Good performance against traditional metrics can still coexist with poor representation of actual observations without thorough validation.
- Robust ML models should maintain reasonable metrics across conditions while capturing the physical mechanisms governing the system.
- Parametric and sensitivity analyses help test whether predictions agree with real behavior rather than merely fitting observed variables.
- Performance varies across problems and metrics, so no single metric or model-selection criterion is universally sufficient.
4. Conclusions
The conclusions anticipate broader ML use in materials science and structural engineering while stressing thorough application and validation. They recommend multiple fitness criteria and continued interdisciplinary and cross-learning research.
- ML is expected to become a key analysis tool for material scientists and structural engineers in coming years.
- The paper recommends thorough ML integration supported by proper validation procedures.
- A variety of performance and error metrics exists for regression and classification, motivating multi-fitness criteria for model validity.
- Interdisciplinary collaboration could further improve existing and future fitness functions.
- Documenting metrics for unsupervised and reinforcement learning remains an ongoing research need.