MATLAB Implementation of SVM Prediction with Code Description

Support Vector Machine (SVM) is a powerful machine learning algorithm widely used for classification and regression tasks. Implementing SVM prediction in MATLAB involves several key steps, with special attention required for data dimension compatibility.

### Core Workflow

Data Preparation First, organize training and test data ensuring consistent feature vector dimensions. MATLAB's SVM implementation typically requires input data in matrix format where each row represents a sample and each column corresponds to a feature. Key considerations include normalizing numerical data and handling categorical variables through appropriate encoding techniques.

Model Training Use the `fitcsvm` function to train the SVM classification model. This function supports critical parameter adjustments including kernel type selection (linear, polynomial, or radial basis function) and penalty parameter C optimization. For example: `svmModel = fitcsvm(X_train, y_train, 'KernelFunction', 'rbf', 'BoxConstraint', C);` The algorithm works by finding the optimal hyperplane that maximizes the margin between different classes while minimizing classification errors.

Model Prediction After training, employ the `predict` function for new data predictions: `y_pred = predict(svmModel, X_test);` Input data must maintain identical feature dimensions as training data to avoid dimension mismatch errors. The prediction process involves transforming input features using the selected kernel function and applying the decision boundary.

Performance Evaluation Assess model performance using confusion matrices or classification accuracy metrics. MATLAB provides functions like `confusionmat` for detailed analysis: `cm = confusionmat(y_true, y_pred);` Additional evaluation metrics can include precision, recall, and F1-score calculations for comprehensive model assessment.

### Important Considerations Feature dimensions must match precisely between training and prediction phases to ensure proper model functionality. Kernel function selection significantly impacts model performance - linear kernels suit linearly separable data while RBF kernels handle complex non-linear patterns. Parameter tuning (C value and kernel parameters) critically affects model generalization capability, often requiring cross-validation techniques like `crossval` for optimal parameter selection.

With proper configuration and parameter adjustment, this SVM prediction implementation operates reliably and adapts effectively to various classification tasks across different domains.