The array of a priori class probabilities, sorted by the class label value. The parameter can be used to tune the decision tree preferences toward a certain class. For example, if users want to detect some rare anomaly occurrence, the training base will likely contain much more normal cases than anomalies, so a very good classification performance will be achieved just by considering every case as normal. To avoid this, the priors can be specified, where the anomaly probability is artificially increased (up to 0.5 or even greater), so the weight of the misclassified anomalies becomes much bigger, and the tree is adjusted properly.

public float[] Priors { get; set; }

Public Property Priors As Single()
	Get
	Set

public:
property array<float>^ Priors {
	array<float>^ get ();
	void set (array<float>^ value);
}

member Priors : float32[] with get, set