MATLAB Code Implementation of Support Vector Machine Regression

Support Vector Machine Regression (SVR) is a powerful machine learning regression method capable of effectively handling nonlinear relationships and high-dimensional data. MATLAB implementation of SVR typically relies on the built-in Statistics and Machine Learning Toolbox, particularly the `fitrsvm` function. ### Core Implementation Workflow Data Preparation The implementation begins with loading and preprocessing data to ensure correct formatting of input features and target variables. Data is typically split into training and testing sets for subsequent model evaluation. Code implementation includes using functions like `readtable` or `csvread` for data loading, followed by normalization using `zscore` or `mapminmax` for better numerical stability. Model Training The SVR model is trained using the `fitrsvm` function, which implements the epsilon-insensitive support vector regression algorithm. Key parameters to configure include: - Kernel function type (linear, Gaussian/RBF, polynomial) - Penalty parameter C (controls tolerance for errors) - Kernel scale (gamma parameter for RBF kernel) - Epsilon parameter (defines the margin of tolerance) Example code: `svrModel = fitrsvm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', C);` Model Evaluation After training, model performance is evaluated on the test set using metrics such as Mean Squared Error (MSE) or Coefficient of Determination (R²). MATLAB provides the `predict` function for generating predictions: `y_pred = predict(svrModel, X_test);`. For cross-validation analysis on training data, `resubPredict` can be used to assess model fit without additional data splitting. Result Visualization For low-dimensional data, scatter plots with superimposed regression curves can intuitively demonstrate fitting effectiveness using `plot` and `hold on` functions. For high-dimensional data, analyzing residual distributions through `histogram(residuals)` or feature importance ranking via `predictorImportance` provides better insights. ### Extended Applications Parameter Tuning: Automated hyperparameter optimization using `bayesopt` for Bayesian optimization or `crossval` for cross-validation techniques Multi-output Regression: Handling multi-target prediction tasks through ensemble methods or modified SVR structures using one-vs-all approaches Real-time Prediction: Exporting trained models as C code using MATLAB Coder for deployment on embedded systems Note: MATLAB's SVR implementation requires Statistics and Machine Learning Toolbox licensing. For lightweight solutions, consider third-party libraries such as LIBSVM's MATLAB interface, which can be integrated using `svmtrain` and `svmpredict` functions.