Using SVM to solve Classification Problems

The SVM task trains a Support Vector Machine for classification. The SVM model uses a kernel function (a generalization of scalar product) to find the optimal separating surfaces in data.

The output of the task is a model, containing a weight matrix wji ,that can be employed by the Apply Model task to perform the SVM forecast on a set of examples.


Additional tabs

  • The Monitor tab, where it is possible to view the temporal evolution of some quantities related to the SVM optimization during its execution. In particular, the behavior of tolerance, and its minimum is reported as a function of the number of iterations. These plots can be viewed during and after computation operations. 

  • The Results tab, where statistics on the SVM computation are displayed, such as the execution time, number of attributes etc..


  1. Drag and drop the SVM task onto the stage.

  2. Connect a task, which contains the attributes from which you want to create the model, to the new task.

  3. Double click the SVM task. 

  4. Configure the options described in the table below.

  5. Save and compute the task.

SVM basic options

Parameter Name


Input attributes

Drag and drop the input attributes you want to use to build the network.

Output attributes

Drag and drop the attributes you want to use to build the model.

SVM formulation

Select the formulation for the SVM problem. Possible choices are:

  • C_SVC: in this case the problem to be solved is:

  • NU_SVC: in this case the problem to be solved is given by:

Degree in kernel function

Specify the value of the parameter d in the kernel function.

Note this parameter is only required for Polynominal kernel functions.

Kernel function

Indicate the kernel function to be used.

Possible choices are:

  • Linear: K(xi , xj) = xi . xj

  • PolynominalK(xi , xj) = (γxi . xj + C0)d

  • Radial basis functionK(xi , xj) = exp(-γ||xi - xj||2)

  • SigmoidK(xi , xj) = tanhxi . xj + C0)

Gamma in kernel function

Specify the value of the parameter γ in the kernel function.

Note this parameter is only required for Polynominal, Radial basis function and Sigmoid kernel functions.

Normalization for input variables

The type of normalization to use when treating ordered (discrete or continuous) variables.

Every attribute can have its own value for this option, which can be set in the Data Manager task. These choices are preserved if Attribute is selected in the Normalization of input variables option; otherwise any selections made here overwrite previous selections made.

Coef0 in kernel function

Specify the value of the parameter c0 in the kernel function.

Note this parameter is only required for Polynominal and Sigmoid kernel functions.

SVM advanced options

Parameter Name


Parameter C of C-SVC

Specify the value of the parameter in the SVM formulation.

Tolerance threshold

Specify the tolerance of the terminating criterion.

Parameter nu of nu-SVC

Specify the value of the parameter ν in the nu-SVM formulation.

Cache memory size

Specify the amount of cache that can be used during training.

Use shrinking heuristics

If selected, heuristic methods will be used to speed up computation.

Append results

If selected, the results of this computation are appended to the dataset, otherwise they replace the results of previous computations.

Aggregate data before processing

If selected, identical patterns are aggregated and considered as a single pattern during the training phase.


The following example uses the Adult dataset.



After having imported the file with an Import from Text File task and after having split the dataset into training (70%) and test (30%) sets with the Split Data task, drag a SVM task onto the stage.

Open it and drag the Income attribute onto the Output area, then drag the following attributes in the Input area:

  • age

  • workclass

  • education

  • occupation

  • race

  • sex

  • native-country

Configure these options as follows:

  • SVM formulation: C_SVC

  • Kernel function: Linear

Then, open the Advanced tab and configure the following option:

  • Parameter of C of C-SVC: 0.5000

Leave the remaining default settings, then save and compute the task.



The execution of the SVM task can be viewed in the Monitor tab.

In these plots the behavior of the tolerance (and its minimum) as a function of the iteration is shown.

The forecast ability of the set of generated rules can be viewed by adding an Apply Model task to the SVM task, and computing with default options.

The forecast produced by the Apply Model task can be analyzed by right-clicking the task and selecting Take a look.

In the data table the following columns relative to the results of SVM elaboration have been added:

  • the SVM output forecast: pred(income)

  • the confidence of this forecast: conf(income)