Decision Tree’s Features of Application in Classification Problems

Abstract

mentation and data clustering. This is facilitated by such distinctive features as interpretability, controllability and an automatic feature selection. However, there are number of fundamental shortcomings, due to which the problem of decision trees learning becomes much more complicated. The article provides the analysis of advantages and disadvantages of decision trees, the issues of decision trees learning and testing are considered. Particular attention is given to balance of training dataset. We also consider the decision forests and methods of its learning. A brief overview of methods for reducing errors interdependence of decision trees in decision forests learning is given. Methods for overcoming of drawbacks of decision trees are offered, results of these methods are proposed.

Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

“…Decision trees are used for classification and regression problems. They are composed of vertices, which are written verifiable conditions, and leaves, in which answers are recorded (M classes for classification tasks) [42]. Boosting improves the performance of machine learning algorithms.…”