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How does SVM measure performance?
If you want to evaluate the performance, your first data sets is used to train the SVM, and the second learning data, which are not perfect (e.g. Noise) is taken for testing the SVM trained. To get performance, you have the accuracy, the precision, the recall, the f1-score (or f-measure) and the cohen’s kapa.
How is SVM different?
SVM try to maximize the margin between the closest support vectors whereas logistic regression maximize the posterior class probability. SVM is deterministic (but we can use Platts model for probability score) while LR is probabilistic. For the kernel space, SVM is faster.
Which SVM kernel is best?
Popular SVM Kernel Functions
- Linear Kernel. It is the most basic type of kernel, usually one dimensional in nature.
- Polynomial Kernel. It is a more generalized representation of the linear kernel.
- Gaussian Radial Basis Function (RBF) It is one of the most preferred and used kernel functions in svm.
- Sigmoid Kernel.
Which is better, a SVM or a NN?
For SVM compared to NN: SVMs are much slower. There is a straightforward reason for this: SVM training requires solving the associated Lagrangian dual (rather than primal) problem.
What’s the difference between a SVM and a neural network?
Training an SVM, by contrast, means an explicit determination of the decision boundaries directly from the training data. This is of course required as the predicate step to the optimization problem required to build an SVM model: minimizing the aggregate distance between the maximum-margin hyperplane and the support vectors.
What do you mean by failure rate in SVM?
For SVM, classification involves determining on which side of the decision boundary a given point lies, in other words a cosine product . By “failure rate” i assume you mean error rate rather than failure of the classifier in production use.
Why does SVM training require solving Lagrangian dual problem?
There is a straightforward reason for this: SVM training requires solving the associated Lagrangian dual (rather than primal) problem. This is a quadratic optimization problem in which the number of variables is very large–i.e., equal to the number of training instances (the ‘length’ of your data matrix).