Which kind of data does sigmoid kernel performance well? While I was playing with some hyperparameters, I came to a wired situation. My dataset is IRIS dataset to be specific. SVM algorithm has some hyperparameters that we can tune, such as Kernels, and C value. 
(All accuracy calculations and SVM are from sklearn package to be specific)
I made a comparison between kernels and noticed sigmoid kernel was performing way worse in terms of accuracy. It is more than 3 times less accuracy than RBF, Linear, and Polynomial. I do know that kernels are quite data-sensitive and data-specific, but I would like to know &quot;Which types of data is sigmoid kernel good at any example? or is this my fault due to wrong C value for sigmoid kernel?&quot;
The sigmoid kernel is better suited for binary classifications. As the IRIS dataset is for multi-class classification, its performance was not as good as other kernels. You can train with only 2 types of flowers to see if the sigmoid kernel can perform well.