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Top 7 Support Vector Machine (SVM) Interview Questions for Data Science and Machine Learning


The support vector machine (SVM) model is a frequently asked interview topic for data scientists and machine learning engineers. In this tutorial, we will talk about the top 7 support vector machine (SVM) interview questions and how to answer them. The 7 questions are:

  1. How does the support vector machine (SVM) algorithm work?
  2. What are margin, soft margin, and support vectors for support vector machine (SVM)?
  3. How do C and Gamma affect a support vector machine (SVM) model?
  4. What is the Kernel trick for support vector machine (SVM)?
  5. What is the hinge loss of a support vector machine (SVM) model?
  6. What are the advantages of a support vector machine (SVM) model?
  7. What are the disadvantages of a support vector machine (SVM) model?

Resources for this post:

Top 7 Support Vector Machine (SVM) Interview Questions – GrabNGoInfo.com

Let’s get started!


Question 1: How does the support vector machine (SVM) algorithm work?

At a high level, the support vector machine (SVM) algorithm follows three steps:

  1. Create all possible hyperplanes that separate the classes.
  2. Compare the margin of the hyperplanes and pick the hyperplane with the largest margin.
  3. Make predictions for the new data points based on which side of the hyperplane the new data point falls in.

Question 2: What are margin, soft margin, and support vectors for support vector machine (SVM)?

  • Margin is the shortest distance between the hyperplane and the closest data points (support vectors). Maximal Margin Classifier picks a hyperplane that maximizes the margin. One drawback of the Maximal Margin Classifier is that it is sensitive to the outliers in the training dataset.
  • Soft margin refers to the margin that allows misclassifications. Support vector machine (SVM) uses a soft margin. The number of misclassifications allowed in the soft margin is determined by comparing the cross-validation results. The one with the best cross-validation result will be selected.
  • Support vectors refer to the data points on the edge and within the soft margin of a support vector machine (SVM). The data points on the edge determine the decision boundary.

Question 3: How do C and Gamma affect a support vector machine (SVM) model?

  • C is the l2 regularization parameter. The value of C is inversely proportional to the strength of the regularization.

👉 When C is small, the penalty for misclassification is small, and the strength of the regularization is large. So a decision boundary with a large margin will be selected.

👉 When C is large, the penalty for misclassification is large, and the strength of the regularization is small. A decision boundary with a small margin will be selected to reduce misclassification.

  • Gamma is the kernel function coefficient. The kernel function transforms the training dataset into higher dimensions to make it linearly separable. The kernel function can take the values such as RBF (Radial Basis Function), poly, linear, and sigmoid. Gamma can be seen as the inverse of the support vector influence radius. The gamma parameter highly impacts the model performance.

👉 When gamma is small, the support vector influence radius is high. If the gamma value is too small, the radius of the support vectors covers the whole training dataset, and the pattern of the data will not be captured.

👉 When gamma is large, the support vector influence radius is low. If the gamma value is too large, the support vector radius is too small and tends to overfit.

Question 4: What is the Kernel trick for support vector machine (SVM)?

  • Support vector machine (SVM) can only provide linear boundaries by default. For a dataset that is not linearly separable, a support vector machine (SVM) needs to understand the higher dimensional relationship in order to separate it by a hyperplane.
  • The kernel trick is the trick of describing how the data points relate to each other at the high-dimensional space without actually transforming the data to a higher dimension. This reduces the computation time of the support vector machine (SVM).

Question 5: What is the hinge loss of a support vector machine (SVM) model?

Hinge loss is the loss function used by the support vector machine (SVM) model to find the best decision boundary.

  • The loss for a correct prediction is zero. The correct predictions are on the correct side of the boundary.
  • The loss for a wrong prediction is positive. The farther the data point is from the decision boundary, the higher the loss.
  • The loss for the correct prediction on the margin has a loss between 0 and 1. There is still a loss because the prediction is not well separated from the other class.
  • The loss for the wrong prediction on the margin has a loss slightly higher than 1.
  • The data points on the hyperplane have a loss of 1.

Question 6: What are the advantages of a support vector machine (SVM) model?

  • Support vector machine (SVM) can produce good results for the dataset with very high dimensions (a lot of features). It even works when the number of dimensions is greater than the number of samples.
  • Support vector machine (SVM) can work for smaller datasets because deciding the decision boundary does not need a lot of data points.
  • Support vector machine (SVM) works well for the dataset that is not linearly separable because of the kernel trick.
  • The decision boundary found by the support vector machine (SVM) is guaranteed to be the global minimum and not the local minimum.
  • Support vector machine (SVM) is fast when doing predictions. This is because it uses a small number of support vectors to make predictions so the amount of memory used is low.

Question 7: What are the disadvantages of a support vector machine (SVM) model?

  • The support vector machine (SVM) model is not easy to interpret and does not have predicted probability. There is no predicted probability available because the prediction is based on which side of the hyperplane the new data point falls into.
  • Support vector machine (SVM) model performance is quite sensitive to hyperparameter optimization such as choosing kernel, C, and gamma. The model can fail to make valid predictions or overfit with sub-optimal parameters.
  • The support vector machine (SVM) model is not scalable and does not work well on large datasets.
  • Data standardization is needed for support vector machine (SVM), otherwise, the features with large values will dominate the model.
  • Support vector machine (SVM) does not perform well when the data has outliers, lots of noises, or there are overlaps between classes.

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