Yield Strength Prediction Using Machine Learning
Machine learning workflow for predicting yield strength using Linear, SVR, Random Forest and Gradient Boosting models in MATLAB.
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This project provides a clear and reproducible MATLAB workflow for predicting yield strength (Sy) of engineering materials using machine learning techniques.
The script performs:
- Data cleaning and preprocessing
- One-hot encoding of categorical variables
- 80/20 holdout validation
- Model training and comparison
Implemented models:
• Linear Regression (Ridge)
• Support Vector Regression (Bayesian optimization)
• Random Forest (TreeBagger)
• Gradient Boosting (LSBoost)
Performance is evaluated using MAE, RMSE, and R² metrics.
A simple ablation experiment is also included to analyze feature influence.
This work was developed as part of a machine learning training program. The code is intentionally written as a readable baseline implementation suitable for educational use and model comparison.
引用
Besim Ali, Dr Mert Akın İnsel (2026). Yield Strength Prediction Using Machine Learning (MATLAB) (https://www.mathworks.com/matlabcentral/fileexchange/<...>), MATLAB Central File Exchange. Retrieved February 13, 2026.
