Sentence examples for decision tree inductive from inspiring English sources

If we represent each example by a set of descriptive attributes and its target attribute and their attribute values, then we can define decision tree inductive learning as follows.

Pruning decision trees is a useful technique for improving the generalization performance in decision tree induction, and for trading accuracy for simplicity in other applications.

Bresso et al.13 constructed a database of drug, ADR and target knowledge and used decision trees and inductive logic programming to predict ADR profiles (rather than individual ADRs) and validated predicted ADRs using FAERS3.

The well-known inductive learning environment [7] and a similar decision tree based rule induction algorithm [8], as well as conceptual clustering algorithms (e.g., COBWEB [9], AutoClass [10, 11], and ITERATE [12]) and statistical packages, make this assumption.

Decision tree is one of the most used inductive learning methods.

Decision tree learning is among the most widely used and practical methods of inductive inference [ 11].

Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) models were constructed.

The features were classified by decision tree algorithms.

All decision tree approaches investigated are in the WEKA package.

Do a simple decision tree.

Out of these, useful features were identified using the J48 decision tree algorithm and selected features are used with various decision trees.