Top 5 Advantages and Disadvantages of Support Vector Machine Algorithm

Support vector machines or SVM is a supervised machine learning algorithm that can be used for both classification and regression analysis.

Advantages of Support Vector algorithm

  1. Support vector machine is very effective even with high dimensional data.
  2. When you have a data set where number of features is more than the number of rows of data, SVM can perform in that case as well.
  3. When classes in the data are points are well separated SVM works really well.
  4. SVM can be used for both regression and classification problem.
  5. And last but not the least SVM can work well with image data as well.

Disadvantages of Support Vector algorithm

  1. When classes in the data are points are not well separated, which means overlapping classes are there, SVM does not perform well.
  2. We need to choose an optimal kernel for SVM and this task is difficult.
  3. SVM on large data set comparatively takes more time to train.
  4. SVM or Support vector machine is not a probabilistic model so we can not explanation the classification in terms of probability.
  5. It is difficult to understand and interpret the SVM model compared to Decision tree as SVM is more complex.

Hence before implementing Support Vector Machine we need to brainstorm , whether it is suitable for our problem statement or not.

Happy Coding !!

You may like to watch a video on Decision Tree from Scratch in Python

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