A High-Quality MATLAB Source Code for Fitting and Prediction

Implementing a high-quality fitting and prediction program in MATLAB typically involves several key steps. The process begins with proper data preprocessing methods, including data cleaning, normalization, and feature selection procedures. For continuous data prediction, common algorithm choices include linear regression, polynomial regression, or support vector regression. The core of the program lies in the model training phase, where MATLAB provides rich built-in functions such as `fitlm` for linear regression modeling and `fitrsvm` for support vector regression implementation. Cross-validation techniques should be employed to evaluate model robustness and prevent overfitting issues. For example, using `crossval` function with k-fold validation can effectively assess generalization performance. During the prediction phase, the trained model is utilized to perform inference on new data while calculating error metrics like Mean Squared Error (MSE) or R-squared (R²) to quantify prediction accuracy. Advanced techniques including ensemble learning methods (using `fitrensemble`) or neural network applications (via `fitrnet`) can further enhance the capability to capture complex data patterns. The final output of fitting curves or prediction results should include statistical information such as confidence intervals, providing reliability references for decision-making. This entire workflow demonstrates a complete machine learning pipeline from data exploration to model deployment, with MATLAB's comprehensive toolbox supporting each implementation stage through structured function calls and parameter optimization.