Interfering Paths in Decision Trees: A Note on Deodata Predictors
This is an incremental improvement for decision tree algorithms, potentially benefiting users in machine learning applications.
The paper tackles the problem of improving prediction accuracy in decision trees by evaluating branches in parallel over multiple paths, resulting in predictions more aligned with nearest neighborhood deodata algorithms and enabling hybridization.
A technique for improving the prediction accuracy of decision trees is proposed. It consists in evaluating the tree's branches in parallel over multiple paths. The technique enables predictions that are more aligned with the ones generated by the nearest neighborhood variant of the deodata algorithms. The technique also enables the hybridization of the decision tree algorithm with the nearest neighborhood variant.