Evaluation metrics and statistical tests for machine learning.

Area of Science:

• Computer Science
• Statistics
• Medical Imaging

Background:

• Machine learning (ML) research is rapidly expanding, yet many researchers lack statistical expertise for model evaluation.
• Comparing the performance of different ML models requires understanding appropriate statistical metrics and tests.

Purpose of the Study:

• To provide a clear guide on evaluating and comparing supervised machine learning models.
• To demystify statistical testing for ML performance assessment.
• To cover common ML tasks such as classification, regression, and object detection.

Main Methods:

• Introduction of common evaluation metrics for supervised ML tasks (binary, multi-class, multi-label classification, regression, image segmentation, object detection, information retrieval).
• Explanation of how to select appropriate statistical tests for model comparison.
• Guidance on obtaining sufficient metric values for testing, performing tests, and interpreting results.

Main Results:

• Comprehensive overview of essential ML evaluation metrics.
• Detailed methodology for statistical comparison of ML models.
• Practical examples using convolutional neural networks for medical image analysis (X-ray classification, tumor detection).

Conclusions:

• Researchers can confidently evaluate and compare ML models using the presented metrics and statistical methods.
• The study aids in selecting the best-performing ML models for specific applications, particularly in medical imaging.
• Improved understanding of ML performance assessment facilitates more robust research outcomes.