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In this article, we have presented 5 Disadvantages of Support Vector Machine (SVM) and explained each point in depth.
The Disadvantages of Support Vector Machine (SVM) are:
- Unsuitable to Large Datasets
- Large training time
- More features, more complexities
- Bad performance on high noise
- Does not determine Local optima
We will dive into each point in depth.
1) Unsuitable to Large Datasets
Support Vector Machines creates a margin of separation between the data point to be classified.The usage of large datasets has its cons even if we use kernel trick for classification.No matter how computationally efficient is the calculation, it is suitable for small to medium size datasets, as the feature space can be very high dimensional, or even infinite dimensional. The method becomes infeasible for large datasets.For large datasets, this can still give us rich feature space representations, but with many fewer dimensions than data points. It will not support large datasets and many dimensions at the same time.
2) Large training time
Due to high computational complexities and above stated reasons even if kernel trick is used,SVM classification will be tedious as it will use a lot of processing time due to complexities in calculations. This will result large time to train the datasets itself.
3) More features, more complexities
More the features are taken into consideration, it will result in more dimensions coming into play.If the number of features is much greater than the number of samples, avoid over-fitting in choosing Kernel functions and regularization term is crucial.
4) Bad performance on high noise
SVM does not perform very well, when the data set has more noise.When the data has noise, it contains many overlapping points,there is a problem in drawing a clear hyperplane without misclassifying.
Soft margin classtification however allows misclassification to a small extent.
But as the noise increases, the amount of datapoints overlapping and disturbances result in more misclassifications which is not ideal.
5) Does not determine Local optima
If you use gradient descent to solve the SVM optimization problem, then you'll always converge to the global minimum.
With this article at OpenGenus, you must have the complete idea of Disadvantages of SVM.
