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Trade-off between training and testing ratio in machine learning for medical image processing.

Muthuramalingam Sivakumar¹, Sudhaman Parthasarathy², Thiyagarajan Padmapriya²

¹Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, TamilNadu, India.

Peerj. Computer Science

|September 24, 2024

Summary

Choosing the right train-test split ratio is crucial for machine learning model performance. This study shows that varying ratios significantly impacts accuracy, highlighting the need for a balanced approach to avoid overfitting or underfitting.

Keywords:

Medical image processing Overfitting Train-test split Underfitting

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Area of Science:

Computer Science
Data Science
Biomedical Imaging

Background:

Artificial intelligence (AI) and machine learning (ML) are increasingly vital for decision-making.
Model performance is highly dependent on the ratio of training to testing data.
Effective model generalization requires careful consideration of dataset splitting.

Purpose of the Study:

To investigate the impact of different train-test split ratios on ML model performance.
To evaluate how these ratios affect model generalization capabilities.
To identify optimal splitting strategies for diverse ML applications.

Main Methods:

Utilized the BraTS 2013 dataset for analysis.
Trained multiple ML models including Logistic Regression, Random Forest, K-Nearest Neighbors, and Support Vector Machines.
Experimented with train-test split ratios from 60:40 to 95:05.

Main Results:

Observed significant variations in model accuracy across different train-test split ratios.
Demonstrated that extreme ratios can lead to overfitting or underfitting.
Highlighted the trade-offs between performance metrics, statistical significance, and resource allocation.

Conclusions:

Selecting an optimal train-test split ratio is critical for reliable ML model development.
Balancing model performance and generalization requires careful ratio selection.
Findings provide insights into enhancing the effectiveness of ML applications through strategic data splitting.