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# 204.6.8 SVM : Advantages Disadvantages and Applications

##### Advantages, disadvantages and application of SVM
Link to the previous post : https://statinfer.com/204-6-7-soft-margin-classification-noisy-data/

### SVM Validation

• 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 overfitting is less in SVM.

• 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.

### SVM Application

• Protein Structure Prediction
• Intrusion Detection
• Handwriting Recognition
• Detecting Steganography in digital images
• Breast Cancer Diagnosis

### Conclusion

• 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.

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