Interview Questions on SVM

  1. Explain SVM to a non-technical person.
  • Optimal hyperplane for linearly separable patterns
  • Extend to patterns that are not linearly separable by transformations of original data to map into new space(i.e the kernel trick)
  • The main reason to use an SVM instead is that the problem might not be linearly separable. In that case, we will have to use an SVM with a non-linear kernel (e.g. RBF).
  • Another related reason to use SVMs is if you are in a higher-dimensional space. For example, SVMs have been reported to work better for text classification.
  1. Logistic Regression is computationally more expensive than SVM — O(N³) vs O(N²k) where k is the number of support vectors.
  2. The classifier in SVM is designed such that it is defined only in terms of the support vectors, whereas in Logistic Regression, the classifier is defined over all the points and not just the support vectors. This allows SVMs to enjoy some natural speed-ups (in terms of efficient code-writing) that is hard to achieve for Logistic Regression.
  1. https://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.html
  2. http://web.mit.edu/6.034/wwwbob/svm-notes-long-08.pdf
  3. https://en.wikipedia.org/wiki/Support_vector_machine

--

--

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Alekhyo Banerjee

Alekhyo Banerjee

Data Science| Data Analysis| Data Visualisation| OOP|Python|C Second-Year Undergraduate in Computer Science and Engineering at RCCIIT,Kolkata