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
- Support vector machine is very effective even with high dimensional data.
- 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.
- When classes in the data are points are well separated SVM works really well.
- SVM can be used for both regression and classification problem.
- And last but not the least SVM can work well with image data as well.
Disadvantages of Support Vector algorithm
- When classes in the data are points are not well separated, which means overlapping classes are there, SVM does not perform well.
- We need to choose an optimal kernel for SVM and this task is difficult.
- SVM on large data set comparatively takes more time to train.
- SVM or Support vector machine is not a probabilistic model so we can not explanation the classification in terms of probability.
- 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