Machine learning model interpretability using SHAP values: Applied to the task of classifying and predicting the
Li Wang1, Xuchun Sun2, Jing Liang3
1State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, PR China.
None:
The rapid identification and prediction of nutritional components in fresh meat products pose a significant challenge. This study aims to classify different cuts of fresh mutton and predict their nutritional components using SVM and PLS model, focusing on the differences in fatty acid composition among the longissimus lumborum, hindshank, and foreshank. An SVM-SHAP model predicted crude fat, protein, and fatty acids, while interpreting feature contributions. PUFA were significantly higher in the hindshank than in the longissimus lumborum and foreshank. The SVM model achieved a classification accuracy of 92.5 % and successfully predicted key nutritional parameters such as EE, CP, MUFA and PUFA with RPD values exceeding 2.7 in the test set. SHAP value analysis revealed that lipid-related variables and wavelengths in the 2300-2500 nm region were major contributors to the model. Vis-NIR-based SVM modeling technology is a fast, non-destructive, and accurate tool for evaluating fresh mutton.
More Related Videos
Related Concept Videos
Classification of Skeletal Muscle Fibers
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
Classification of Signals
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Calibration Curves: Linear Least Squares
For data that follow a straight line, the standard method for fitting is the linear...
Classification of Bones
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
Classification of Systems-II
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:


