> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
