Learns a SVM regression of its input data.
A data set.
A SVM learning algorithm with supplied parameters.
A trained regressor. Signal Predictor sends the regressor only if signal Data is present.
SVM Regression performs linear regression in a high dimension feature space using an ε-intensive loss. Its estimation accuracy depends on a good setting of C, ε and kernel parameters. The widget outputs class predictions based on a SVM learning algorithm.
- Learner/predictor name
- Train an ε-SVR or v-SVR model and set test error bounds.
- Set kernel, a function that transforms attribute space to a new
feature space to fit the maximum-margin hyperplane, thus allowing the
algorithm to create non-linear regressors. The first kernel in the
list, however, is a
Linear kernel that
does not require this trick, but all the others
Functions that specify the kernel are presented beside their
names, and the constants involved are:
- g for the gamma constant in kernel function (the recommended value is 1/k, where k is the number of the attributes, but since there may be no training set given to the widget the default is 0 and the user has to set this option manually),
- c for the constant c0 in the kernel function (default 0), and
- d for the degree of the kernel (default 3).
- Set permitted deviation from the expected value.
- Produce a report.
- Press Apply to commit changes. Alternatively, tick the box on the left side of the Apply button to communicate changes automatically.