SVM
1.Explain SVM algorithm in detail.
SVM stands for support vector machine, it is a supervised machine learning algorithm which can be used for both Regression and Classification. If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate. SVM uses hyperplanes to separate out different classes based on the provided kernel function.
2.What are the different kernels in SVM?
There are four types of kernels in SVM.
Linear Kernel
Polynomial kernel
Radial basis kernel
Sigmoid kernel
3.How does one adjust the support in a Support Vector Machine?
Adjust the support in a Support Vector Machine by adjusting the budget C.
4.How does one adjust the bias in a Support Vector Machine other than changing the kernel?
Adjusting the bias by adjusting gamma.
5.Are their hyperparameters that adjust the support and bias?
Yes. The budget and gamma.
6.If so, how does one determine their values?
Cross-validate.
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