- 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 Advantages & Disadvantages
- 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.
- Protein Structure Prediction
- Intrusion Detection
- Handwriting Recognition
- Detecting Steganography in digital images
- Breast Cancer Diagnosis
- 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.