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How is SVM better than any other algorithm?

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.