Contents
Does SVM work with categorical variables?
If we are talking about categorical classification (as oposed to simple binary classification) you could use one-vs-all prediction. Support vector machines (SVM), especially the kernel SVMs, are falling mostly out of favor because of the fact that they need ready real valued vectors as features.
What are slack variables in SVM?
Slack variables are introduced to allow certain constraints to be violated. That is, certain train- ing points will be allowed to be within the margin. We want the number of points within the margin to be as small as possible, and of course we want their penetration of the margin to be as small as possible.
What are predictors in SVM?
This node uses a SVM model generated by the SVM learner node to predict the output for given values. Only the columns that have been used during training are used in the SVM learner. The output table will contain one extra column, which contains the prediction and optinally the class probabilities.
What are the parameters of SVM?
The SVM algorithm usually depends on several parameters. One of them, denoted C, controls the tradeoff between margin maximization and error minimization. Other parameters appear in the non-linear mapping into feature space. They are called kernel parameters.
What is a hyperplane SVM?
A Support Vector Machine (SVM) performs classification by finding the hyperplane that maximizes the margin between the two classes. The vectors (cases) that define the hyperplane are the support vectors. Algorithm. Define an optimal hyperplane: maximize margin.
Can a SVM be used to calculate variable importance?
One of the advantages of (especially linear) SVMs is that they work well with large numbers of features (providing you tune the regularisation parameter properly), so there is often no need if you are only interested in prediction. If you use R, the variable importance can be calculated with Importance method in rminer package.
How to preprocesse a categorical predictor in SVM?
If V1 = vhigh for a particular row, then V1.vhigh = 1 with V1.low = 0 and V1.med = 0. Since there is no numeric predictor variables in the dataset, we don’t need to consider the issue of standardization of numerical variables.
How are variable selection criteria derived from SVMs?
Also look at this paper which uses criteria derived from SVMs to guide the attribute selection. Isabelle Guyon, André Elisseeff, “An Introduction to Variable and Feature Selection”, JMLR, 3 (Mar):1157-1182, 2003. http://jmlr.csail.mit.edu/papers/v3/guyon03a.html is well worth reading, it will give a good overview of approaches and issues.