204.6.8 SVM : Advantages Disadvantages and Applications

Link to the previous post : https://statinfer.com/204-6-7-soft-margin-classification-noisy-data/

SVM doesn’t give us the probability, it directly gives us the resultant classes.
Usual methods of validation like sensitivity, specificity, cross validation, ROC and AUC are the validation methods.

SVM’s are very good when we have no idea on the data.
Works well with even unstructured and semi structured data like text, Images and trees.
The kernel trick is real strength of SVM. With an appropriate kernel function, we can solve any complex problem.
Unlike in neural networks, SVM is not solved for local optima.
It scales relatively well to high dimensional data.
SVM models have generalization in practice, the risk of over-fitting is less in SVM.
SVM is always compared with ANN. When compared to ANN models, SVMs give better results.

Choosing a “good” kernel function is not easy.
Long training time for large datasets.
Difficult to understand and interpret the final model, variable weights and individual impact.
Since the final model is not so easy to see, we can not do small calibrations to the model hence its tough to incorporate our business logic.
The SVM hyper parameters are Cost -C and gamma. It is not that easy to fine-tune these hyper-parameters. It is hard to visualize their impact

Many software tools are available for SVM implementation.
SVMs are really good for text classification.
SVMs are good at finding the best linear separator. The kernel trick makes SVMs non-linear learning algorithms.
Choosing an appropriate kernel is the key for good SVM and choosing the right kernel function is not easy.
We need to be patient while building SVMs on large datasets. They take a lot of time for training.

In the next and last post we will cover a real problem and solve it with SVM.

The next post is about digit recognition using svm.

9th April 2019