Support Vector Classification

Support Vector Classification (SVC) finds the optimal boundary (hyperplane) that separates data points of different classes with the maximum margin. It uses kernel functions to handle complex, non-linear decision boundaries.

SVC is highly effective in high-dimensional spaces and works well when the number of features is large.

SVC can be slower to train on very large datasets compared to tree-based methods. For best results, always scale your features using the Feature Scaler node before applying SVC.

Use Cases

• Precise boundary classification: When you need a clear, well-defined boundary between classes (e.g., separating facade types based on geometric ratios).
• Small to medium datasets: SVC excels when the dataset is not too large but requires high classification accuracy.