> For the complete documentation index, see [llms.txt](https://maheshwarappa-a.gitbook.io/data-science-interview/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://maheshwarappa-a.gitbook.io/data-science-interview/machine-learning/svm.md).

# 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.

1. Linear Kernel
2. Polynomial kernel
3. Radial basis kernel
4. 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.