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.

“Which is Better”, By Better if we mean Performance wise Better, Then all the conventional Learning Algorithm (which SVM is part of) saturate at a point and doesn’t show any improvement whatsoever by Increasing the Dataset after a point, Where as a Simple NN or a Medium NN gives a Better result, and using a Deep Learning Model, Performance almost Linearly increases , with the Increase in Dataset.

Quite effective at what it’s meant for. Used for “splitting” data, it is quite versatile when it comes to classification.