SVMs offer several advantages that make them particularly useful in the field of nanotechnology:
High Dimensionality Handling: Nanotechnology often deals with complex datasets that have a high number of dimensions. SVMs are well-suited to handle such data efficiently. Robustness: SVMs are robust to overfitting, especially in high-dimensional space, which is crucial for accurate predictions in nanotechnology applications. Versatility: They can be used for both classification and regression tasks, making them versatile tools for various predictive modeling needs in nanotechnology.
