This quiz consists of questions and answers on Support Vector Machine (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.
Support vector machine (SVM) is a _________ classifier?
SVM can be used to solve ___________ problems.
SVM is a ___________ learning algorithm
SVM is termed as ________ classifier
The training examples closest to the separating hyperplane are called as _______
Which of the following is a type of SVM?
The goal of the SVM is to __________
When using R, which of the following package is used for SVM?
Based on the form of error function, SVM models can be classified into which of the following?
In case of classification problem, which of the following may be used?
Which of the following SVM model can be used to detect the outliers?
Which of the following SVM model is more suitable for non-linearly separable data?
Which of the following SVM model tends to overfit?
Which of the following SVM model will have high variance?
For Python SVM classifier, what value of C makes a soft margin classifier similar to maximum margin classifier?
For Python SVM classifier, which of the following is true?
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