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The default is to not specify a complexity factor.
You specify the complexity factor for an SVM model by selecting Specify the complexity factors.
The complexity factor determines the trade-off between minimizing model error on the training data and minimizing model complexity. Its responsibility is to avoid over-fit (an over-complex model fitting noise in the training data) and under-fit (a model that is too simple).
A very large value of the complexity factor places an extreme penalty on errors, so that SVM seeks a perfect separation of target classes. A small value for the complexity factor places a low penalty on errors and high constraints on the model parameters, which can lead to under-fit.
If the histogram of the target attribute is skewed to the left or to the right, try increasing complexity.
The default is to specify no complexity factor, in which case the system calculates a complexity factor. If you do specify a complexity factor, specify a positive number. If you specify a complexity factor for Anomaly Detection, the default is 1.