Support Vector Machine (SVM) is a machine learning algorithm that can be used to classify data. SVM does this by maximizing the margin between two classes, where “margin” refers to the distance from both support vectors. SVM has been applied in many areas of computer science and beyond, including medical diagnosis software for tuberculosis detection, fraud detection systems, and more. This blog post consists of quiz comprising of questions and answers on SVM. This is a practice test (objective questions and answers) that can be useful when preparing for interviews. The questions in this and upcoming practice tests could prove to be useful, primarily, for data scientists or machine learning interns/ freshers/ beginners. The questions are focused on some of the following areas:
- Introduction to SVM
- Types of SVM such as maximum-margin classifier, soft-margin classifier, support vector machine
Some of the key SVM concepts to understand while preparing for the machine learning interviews are following:
- SVM concepts and objective functions
- SVM kernel functions, tricks
- Concepts of C and Gamma value
- Scikit learn libraries for training SVM models
Here are some of the useful posts on SVM you could read for understanding SVM in a better manner:
- SVM Algorithm as Maximum Margin Classifier
- SVM Classifier using Scikit Learn – Code Examples
- SVM – Understanding C Value with Code Examples
- SVM as Soft Margin Classifier and C Value
- Machine Learning – SVM Kernel Trick Example
- SVM RBF Kernel Parameters with Code Examples
Support Vector Machine – Practice Test
Here is the list of 15+ questions that can help you test your SVM knowledge, especially, if you are working with Python.
[wp_quiz id=”5914″]
Support Vector Machine – Most frequently asked interview questions (FAIQ)
Here are some of the most asked interview questions in relation to SVM:
- What are support vector machines? What is the difference between SVC, SVR, and SVRG? Which one should you use for what kind of data?
- What are the steps involved in SVM? How do you choose kernel function parameters?
- How can SVRG be used to solve optimization problems (like maximizing profit or minimizing cost)? Can this also be done using other machine learning algorithms like the random forest, artificial neural networks, etc.?
- What are the pros and cons of SVM in comparison with other machine learning algorithms such as random forest, artificial neural networks, etc.?
- How is SVRG better than SVC when solving optimization problems (like maximizing profit or minimizing cost)? How can you solve this using SVM? What parameters do we need to define for SVRG?
- Where can SVM be applied in real-life scenarios, like fraud detection systems or medical diagnosis software for tuberculosis (TB) detection? Can this also be done in other machine learning algorithms like the random forest, artificial neural networks, etc.?
- When is it recommended to use SVM over other machine learning algorithms like random forest, artificial neural networks, etc.?
- How do you decide which kernel function to use for SVM?
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I found it very helpful. However the differences are not too understandable for me