Support Vector Machine
SVM’s are very good when we have no idea about the data.
- Works well with even unstructured and semi-structured data like text, Images, and trees.
- The kernel trick is the real strength of SVM. With an appropriate kernel function, we can solve any complex problem.
- Unlike in neural networks, SVM is not solved for local optima.
- It scales relatively well to high dimensional data.
- SVM models have generalization in practice, the risk of overfitting is less in SVM.
- SVM is always compared with ANN. When compared to ANN models, SVMs give better results.