# svm interview questions

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## How do Support Vector Machines Work?

SVMs work using the steps mentioned below:

• They map data to a high dimensional feature space for categorization even when it is not possible to distinguish the data points using a linear separator/model.
• Once the separator is found, the data is transformed in such a way wherein the separator can then be drawn as a hyperplane.
• Then, using the characteristics of the new data, predictions can be made which classify a data point to a particular class.
• ## Why do we need to use Support Vector Machines?

We need SVMs due to the benefits we get from incorporating them in our ML models. One such benefit is that they can be used for not only linear classifications or regressions but also non-linear ones. This enables SVMs to decipher much more complex relationships between the data points in the data that we present to them without the onus being on us to perform the complicated transformations. This also results in SVMs being effective in high dimensional spaces, even when the dimensions are higher than the number of samples in the data.

## FAQ

What is SVM best for?

SVM is a supervised machine learning algorithm which can be used for classification or regression problems. It uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.

How does SVM work step by step?

The SVM algorithm steps include the following:
1. Step 1: Load the important libraries. …
2. Step 2: Import dataset and extract the X variables and Y separately. …
3. Step 3: Divide the dataset into train and test. …
4. Step 4: Initializing the SVM classifier model. …
5. Step 5: Fitting the SVM classifier model. …
6. Step 6: Coming up with predictions.

What is SVM and when it is used?

Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection. The advantages of support vector machines are: Effective in high dimensional spaces. Still effective in cases where number of dimensions is greater than the number of samples.

What is SVM types of SVM?

There are two different types of SVMs, each used for different things: Simple SVM: Typically used for linear regression and classification problems. Kernel SVM: Has more flexibility for non-linear data because you can add more features to fit a hyperplane instead of a two-dimensional space.