Using Regression SVM to solve Regression Problems
The SVM task trains a Support Vector Machine for regression. 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.
you must have created a flow;
the required datasets must have been imported into the flow;
the data used for the analysis must have been well prepared;
a unified model must have been created by merging all the datasets into the flow.
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..
Drag and drop the Regression SVM task onto the stage.
Connect a task, which contains the attributes from which you want to create the model, to the new task.
Double click the Regression SVM task.
Configure the options described in the table below.
Save and compute the task.
Regression SVM options
Select the formulation for the SVM problem. Possible choices are:
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.
Indicate the kernel function to be used.
Possible choices are:
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.
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.
Specify the value of the parameter C in the SVM formulation.
Normalization for input variables
The type of normalization to use when treating ordered (discrete or continuous) variables.
Possible methods are:
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.
Parameter epsilon of epsilon-SVR
The ε parameter allows the user to control the number of possible errors in the training set: the higher the number, the higher the number of errors allowed.
Use shrinking heuristics
If selected, heuristic methods will be used to speed up computation.
Parameter nu of nu-SVC
Specify the value of the parameter ν in the nv-SVM formulation. The ν parameter, which must range from 0 to 1, is related to the fraction of examples used as support vectors.
Aggregate data before processing
If selected, identical patterns are aggregated and considered as a single pattern during the training phase.
Specify the tolerance of the terminating criterion.
If selected, the results of this computation are appended to the dataset, otherwise they replace the results of previous computations.
Cache memory size
Specify the amount of cache that can be used during training.
Drag and drop the input attributes you want to use for the classification of data.
Drag and drop the attributes you want to use to form the final classes into which the dataset will be divided.