… Attempts at producing online versions of decision trees largely dominate work in online
learning. Work on incremental decision trees began appearing just as batch decision trees …

… In this paper, we proposed a novel computational method named Ensemble of Decision Tree
based … Then multiple base learnings were built to yield many decision trees (DTs) based on …

… Since the conventional decision tree recursively divides the training data into subsets …
Decision Tree (MDT) represented as a recursive tree structure like the conventional decision tree. …

… Recently, machine learning (ML) practices such as random forest (RF), decision tree (DT), …
In the present study, the objectives are to test two machine learning methods (decision tree …

… decision trees and classification rules for the scenarios. The aim of the study was twofold: (1)
explore how decision trees … field study with the use of decision trees and classification rules. …

… In this research, a Gradient Boost Decision Tree (GBDT) model based on the newly-developed
Light Gradient Boosting Machine algorithm (LightGBM or LGBM) was proposed to model …

… of trees. GBDT methods employ an iterative procedure, where the gradient of the loss function
guides learning a new tree such that adding the new tree to … boosting decision tree models …

… A decision tree Τ can be divided into two parts: tree structure T, which is a graph comprising …
In terms of communication costs, global tree structure determination requires tree depth…

… made by a random forest model is based on the ensemble of decisions made by numerous
decision trees. The word “forest” explains that a RF model consists of many decision trees. …

… system based on decision tree algorithm. This paper uses decision tree technology, data …
can provide valuable information for bank decision makers. The data mining performance of the …