C45Learning Properties

Gets or sets the collection of attributes to be processed by the induced decision tree.

Gets how many times each attribute has already been used in the current path. In the original C4.5 and ID3 algorithms, attributes could be re-used only once, but in the framework implementation this behaviour can be adjusted by setting the Join property.

Gets or sets how many times one single variable can be integrated into the decision process. In the original ID3 algorithm, a variable can join only one time per decision path (path from the root to a leaf). If set to zero, a single variable can participate as many times as needed. Default is 1.

Gets or sets the maximum allowed height when learning a tree. If set to zero, the tree can have an arbitrary length. Default is 0.

Gets or sets the maximum number of variables that can enter the tree. A value of zero indicates there is no limit. Default is 0 (there is no limit on the number of variables).

Gets or sets the decision trees being learned.

Gets or sets the parallelization options for this algorithm.

Gets or sets the step at which the samples will be divided when dividing continuous columns in binary classes. Default is 1.

Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.