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Active Learning is a methodology optimizes the selection of a subset of the support vectors that maintain accuracy while enhancing the speed of the model
Active learning increases performance for a linear kernel. Active learning both increases performance and reduces the size of the Gaussian kernel; this is an important consideration if memory and temporary disk space are issues.
Active learning forces the SVM algorithm to restrict learning to the most informative examples and not to attempt to use the entire body of data. In most cases, the resulting models have predictive accuracy comparable to that of the standard (exact) SVM model.
In most cases, you should not disable this setting
Active learning is selected by default. It can be turned off by unselecting Active Learning.